A MULTICHANNEL OIL DEBRIS SENSOR FOR ONLINE HEALTH MONITORING

OF ROTATING MACHINERY

A Dissertation

Presented to

The Graduate Faculty of The University of Akron

In Partial Fulfillment

of the Requirements for the Degree

Doctor of Philosophy

Li Du

December, 2012

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A MULTICHANNEL OIL DEBRIS SENSOR FOR ONLINE HEALTH MONITORING

OF ROTATING MACHINERY

Li Du

Dissertation

Approved: Accepted:

______Advisor Department Chair Dr. Jiang Zhe Dr. Celal Batur

______Committee Member Dean of the College Dr. Eric Engeberg Dr. George K. Haritos

______Committee Member Dean of the Graduate School Dr. Jae-Won Choi Dr. George R. Newkome

______Committee Member Date Dr. Joan Carletta

______Committee Member Dr. Chien-Chung Chan

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ABSTRACT

Condition based monitoring has become essential in maintaining and extending the health of high-speed rotating and reciprocating machinery. One effective approach to detect signs of potential failure of a rotating or reciprocating machine is to examine the wear debris in its lubricating oil. Magnetic inductive sensors have gained certain success due to the capability of differentiation of ferrous and non-ferrous wear debris, which is important for condition monitoring of rotating and reciprocating machinery. However, they are limited to debris larger than 100 µm in size because of the low sensitivity of 3-D solenoids. To overcome this problem, the design concept and preliminary testing results of an inductive coulter Counter that uses a planar coil as sensing element are presented.

We first demonstrate the feasibility of the inductive Coulter counting principle to detect metal particles in lubrication oil using a microfluidic device. The device detects the passage of ferrous and nonferrous metal particles by monitoring the inductance change of an embedded planar coil. The device was tested using iron and copper particles ranging in size from 50 µm to 125 µm. The testing results have demonstrated that the device is capable of detecting and distinguishing ferrous and nonferrous metal debris particles in lubrication oil.

Next, to overcome the limitations of the microfluidic device, a meso-scale oil debris sensor was studied. The sensor used a two-layer planar coil with a

iii meso-scale fluidic pipe crossing the planar coil’s center as the sensing element. The testing results indicated that the throughput is significantly higher than that of the microfluidic channel without sacrificing the sensitivity. However, the throughput still needs to be improved to meet the online monitoring requirements. To further improve the throughput, we designed and tested an inductive Coulter counter with seven detection channels to detect and count metal particles in lubrication oil. The testing results indicated that using multichannel sensor we are able to improve the detection throughput of the sensor seven times in contrast to a single channel sensor. Negligible crosstalk among channels was observed.

Finally, a proof-of-principle resonant frequency division multiplexed four-channel inductive sensor for detecting microscale metallic debris in lubrication oil using only one set of detection electronics is presented. Using the resonance frequency division multiplexing method, debris particles in four parallel sensing channels were successfully detected and counted simultaneously. Testing results indicate the sensor is capable of differentiating and detecting ferrous and non-ferrous metallic debris. Crosstalk among four sensing channels is negligible.

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DEDICATION

This dissertation is dedicated to my parents

Hanjie Du and Runxin Shang,

who made all this possible, for their endless love, support, encouragement and patience.

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ACKNOWLEDGEMENTS

It would not have been possible to write this doctoral dissertation without the help and support of the kind people around me, to only some of whom it is possible to give particular mention here. First, I would like thank my advisor and mentor Prof. Jiang Zhe.

His good advice and support has been invaluable on both an academic and a personal level, for which I am extremely grateful.

The research journey at The University of Akron would not be possible without the support of all my colleagues and friends in the lab: Dr. Ashish V. Jagtiani, Mr. Abhay

Vasudev, Mrs. Hui Ouyang, Mr. Zhuochen Wang and Mr. Yu Han. I would also like to extend my gratitude to the staff in the Mechanical Engineering, Mrs. Stacy Meier, Mrs.

Christina Christian and Mr. Cliff Bailey for their assistance and technical support since the start of my graduate work in The University of Akron.

I would also like thank my friends that I made over the years at The University of

Akron, Dr. Jie Wen, Mr. Pei Chen, Dr. Ruifeng Wang, whose friendship over the years have made this possible. I must also thank Mr. Yu Han, Mr. Xiaoliang Zhu and Mr.

Abdullah Al Amin who took time to help me proof read this dissertation.

I would also like to thank Dr. Joan Carletta for her continuing advice with my research. In addition, I must extend my gratitude towards my committee members Dr.

Eric Engeberg, Dr. Jae-Won Choi, Dr. Joan Carletta and Dr. Chien-Chung Chan. A special thank goes to Dr. Chien-Chung Chan who agreed to join my dissertation

vi committee after my research proposal and towards the end of this project.

Finally, I would like to thank my parents, my wife Lixuan Yan, my son Yi Du and my daughter Coco Du for their endless love and personal support at all times.

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TABLE OF CONTENTS Page

LIST OF TABLES ...... x

LIST OF FIGURES ...... xi

CHAPTER

I. INTRODUCTION ...... 1

1.1 Introduction ...... 1

1.2 Motivation ...... 3

1.3 Research objectives ...... 4

1.4 Summary ...... 6

II. BACKGROUND AND LITERATURE REVIEW ...... 8

2.1 Electrical impedance detection ...... 9

2.1.1 Resistive detection ...... 9

2.1.2 Capacitive detection ...... 11

2.1.3 Inductive detection ...... 14

2.2 Ultrasonic detection ...... 18

2.3 Optical and imaging detection ...... 22

2.4 X-ray spectrometer ...... 25

2.5 Summary ...... 27

III. PRELIMINARY RESULTS: DEMONSTRATION OF INDUCTIVE COULTER COUNTING PRINCIPLE USING MICROFLUIDIC CHANNEL DEVICE ...... 29

3.1 Inductive Coulter counting principle ...... 29

3.2 Demonstration of inductive Coulter counting principle by using planar coil ..... 35

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3.3 Micro fluid channel debris sensor ...... 42

3.3.1 Device description and fabrication ...... 42

3.3.2 Experimental results and discussion ...... 45

3.4 Summary ...... 50

IV. DESIGN AND IMPLEMENTATION OF A MESO-SCALE WEAR DEBRIS SENSOR ...... 52

4.1 Device design and measurement scheme ...... 53

4.2 Static test and analysis ...... 55

4.3 Experimental results and discussions...... 59

V. DESIGN AND IMPLEMENTATION OF A SEVEN CHANNEL MULTIPLEXED WEAR DEBRIS SENSOR BASED ON UNDERSAMPLING TECHINIQUE ...... 66

5.1 Device design and measurement setup ...... 67

5.2 Undersampling measurement ...... 71

5.3 Signal processing ...... 79

5.4 Crosstalk analysis...... 80

5.5 Experimental results and discussion ...... 82

5.6 Summary ...... 90

VI. IMPLEMENTATION OF A FOUR CHANNEL MULTIPLEXED WEAR DEBRIS SENSOR BASED ON RESONANCE FREQUENCY DIVISION TECHNIQUE ...... 91

6.1 Device design and measurement setup ...... 92

6.1.1 Device design and resonant frequency division multiplexing concept 92

6.1.2 Measurement parameter determination ...... 94

6.1.3 Signal processing ...... 98

6.2 Simulation based on equivalent circuit ...... 99

6.3 Experimental results and discussions...... 104

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6.3.1 Determination of excitation frequency by experiments ...... 104

6.3.2 Dynamic testing results and discussions ...... 108

6.4 Summary ...... 119

VII. CONCLUSIONS AND FUTURE WORK...... 121

7.1 Conclusions ...... 121

7.2 Future work ...... 123

7.2.1 Improvement on detect limits of inductive debris sensor ...... 123

7.2.2 Integrated inductive-ultrasonic debris sensor ...... 124

REFERENCES ...... 129

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LIST OF TABLES Table Page

3.1 Material parameters of iron and copper………………………………. 31

3.2 Inductance change measured in a solenoid with 1.2mm coil length and coil diameter …………………………………………………..…. 35

3.3 Metal particles used in testing the microscale inductive Coulter counting concept (D: Diameter; L: Length)…………………...... 36

3.4 Metal particles used in testing the microscale device….…………...… 45

5.1 Standard deviation of peak detection…..……………………..………. 77

6.1 Simulation parameters for channel 1 to channel 4..…………..………. 99

6.2 Base voltage variation of Vout(f1) to Vout(f4) by using different FFT windows………………………………...…………….……………….. 100

6.3 Measured parameters to determined excitation frequencies for channel 1 to channel 4……………………………….……………….. 105

6.4 Determined excitation frequencies for channel 1 to channel 4………………………………………………………………....…..… 108

6.5 Relative inductance change caused by iron particles and copper particles in channel 1 to channel 4………...…………………..……… 117

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LIST OF FIGURES Figure Page

2.1 A schematic of a resistive oil condition sensor using eight rod electrodes………………………………………………….………...... 11

2.2 Schematic of a capacitive oil debris sensor [20] ….……………….…. 13

2.3 Measured capacitance change of the microfluidic sensor for lubricating oil with 10-25 μm aluminum particles [20]……………..... 14

2.4 Sensing principle of the inductive oil debris sensor. (a) Magnetic field is induced in solenoid owing to a current passing through solenoid, (b) Magnetic flux is attenuated owing to eddy current generated in a conductive but non-ferrous particle, (c) Magnetic flux is enhanced owing to high relative permeability but also attenuated owing to eddy current in a ferrous and conductive particle [22]…………………………………………………………… 15

2.5 Schematic of an inductive debris sensor to remove the disturbance by air bubbles and water droplets………………………….…………….. 16

2.6 Schematic of a metallic debris sensor system incorporating an oil debris sensor and two pressure sensors…………………...………….. 17

2.7 Schematic of an ultrasonic oil debris monitoring systems using two ultrasonic transducers……………………………………………..….. 18

2.8 Schematic of a pulse-echo ultrasonic oil debris sensor using a single ultrasonic transducer, (a) Measurement setup, (b) Incident echo signals……………………. 19

2.9 Schematic of an optical wear debris sensor based on light transmission measurement…………………………………………..... 22 2.10 Illustration of wear debris sensors based on light blocking or light scattering. ……………………………………………….…………… 23

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2.11 Illustration of a wear debris imaging system…………………………. 24

Schematic diagram of a debris detection system on X-ray 2.12 spectrograph…………………………………………………………... 27 3.1 Sensing principle of the magnetic Coulter counting sensor. (a) Magnetic field induced in planar coil owing to a current passing through solenoid…………………………………………………….... (b) Magnetic flux is enhanced owing to high relative permeability particles…………………………………………………………..…… (c) Magnetic flux is attenuated owing to eddy current generated in a conductive particle……………...... 30

3.2 Frequency response of a solenoid with diameter and length of 1mm. (a) Inductance change caused by 150µm iron particles…………….… (b) Inductance change caused by 150µm copper particles…...... 32

3.3 Inductance change of solenoid calculated at 2MHz excitation frequency. (a) Iron particles with sizes ranging from 50µm to 150µm …………... 33 (b) Copper particles with sizes ranging from 50µm to 150µm ………. 34

3.4 Schematic of the microscale planar coil used for metal particle detection…………………………………………….………………… 36

3.5 Testing setup for the microscale planar coil…………………...... …. 37

3.6 Measured relative inductance change caused by iron and aluminum particles…………………………………………..…………………… 40

3.7 Measured relative inductance change caused by a 100µm aluminum particle and an 80µm iron particle………………………...………….. 40

3.8 Measured relative inductance change caused by a 100µm iron particle at different vertical distances z from the coil………….…...… 41

3.9 Schematic of the microfluidic device for metal particle detection in lubrication oil……………………………………………………….… 43

3.10 Measured relative inductance change of the device for lubrication oil with (a) Iron particles with sizes ranging from 75μm-105μm, (b) Iron particles with sizes ranging from 50μm-75μm…………………….…. 47 (c) 125μm copper particles, (d) 100μm copper particles……………... 48

3.11 Measured relative inductance change of the device for lubrication oil with iron particles from 75μm to 105μm, and 125μm copper

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particles……………………………………………………………..… 49

4.1 Schematic of the oil debris sensor for metal particle detection in lubrication oil………………………………………………………..... 54

4.2 Illustration of sensing zones of inductive oil debris sensors using (a) a 3-D solenoid and (b) a 2-layer planar coil as the sensing elements………………………………………………………………. 55

4.3 Measured inductance change caused by an 85μm iron particle as a function of vertical position………………………………………..…. 57

4.4 Relative magnetic flux density as a function of radial position at the central plane (z=0) along radial direction………………………….…. 58

4.5 Illustration of experimental setup. …………….…………………...… 60

4.6 Measured relative inductance change caused by (a) 75-105μm iron particles, (b) 50-75μm iron particles………….….. 62 (c) 105-150μm copper particles…………………………...………….. 63

4.7 Measured relative inductance change caused by iron particles with size from 50 μm to 75 μm and copper particles with size ranging105 μm to 150 μm……………………………………………………….… 64

5.1 Schematic of the oil debris sensor for metal particle detection in lubrication oil……………………………………………………...….. 67

5.2 Measurement circuit and equivalent circuit of the seven channel sensor …………………………………….………………………...… 68

5.3 Illustration of waveforms of VL1 and VR.……………………………... 69

5.4 Process and results of undersampling………………………………… 72

5.5 Illustration of phase difference before and after the undersampling process. VL1 and VR are the signals from channel 1 and sampling resistance before undersampling, while VL1′ and VR′ are the signals after undersampling. (a) Excitation signals…………………………………………………. 74 (b) Results after undersampling………………………………………. 75

5.6 Illustration of calculating the standard deviation of peak values.…………….……………………………………………..……. 77

5.7 Standard deviation of voltage peak…………...……………………… 78

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5.8 Interpolation of VR obtained from under sampling………………….... 80

5.9 Crosstalk between channel 1 and channel 2…………………….……. 81

5.10 Inductance change of channel 1 and channel 2………………….…… 82

5.11 Inductance change caused by a 105um iron particle (a) LCR meter…………………………………………….…………... 83 (b) DAQ……………………...…………………………………….…. 84

5.12 Measured relative inductance change caused by iron particles with size from 75 μm to 105 μm and copper particles with size ranging125 μm to 150 μm………………………………………………………..... 86

5.13 Measured relative inductance change caused by a 105 µm iron particles, (a) Debris passing through Channel 3……………………………….. 88 (b) Debris passing through Channel 6………………………………... 89

6.1 Schematic of the multiplexed four-channel oil debris sensor for metal debris detection in lubrication oil.…..……………………….……….. 93

6.2 Measurement setup and equivalent circuit of the four channel debris sensor………………………...…………………………….…...…….. 93

6.3 (a) Relative impedance change of the parallel LRC circuit caused by coil inductance change at different excitation frequency, fi. Here Lbase-i=800nH, Cp=7.91nF, Rs=0.9Ω, resonant frequency = 2MHz)………………………………………………………………… 95 (b) Relative impedance change of the LRC circuit as a function of relative inductance change (re-plotted from the data shown in Figure 6.3(a))………………………………………………………….……. 96

6.4 Setup and Simulated inductance change at channel 1 to channel 4 (a) Channel 1……………………………..………….………..……… 101 (b) Channel 2, (c) Channel 3……………….…………..……..……… 102 (d) Channel 4……………………………..…………………...……… 103

6.5 Measured waveform of Vout…………………….…………………….. 103

6.6 Frequency response of relative change of Vout for channel 1 to channel 4 as a function of excitation frequency fi (a) Channel 1……………………………..…………….……..……… 105 (b) Channel 2, (c) Channel 3…………………..……..………….…… 106 (d) Channel 4……………………………..…………..………………. 107

6.7 Schematic of the multiplexed four-channel oil debris sensor for metal

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debris detection in lubrication oil.…..……………………….……….. 109

6.8 Frequency spectrum of Vout…………………………………………... 110

6.9 Measured relative change o Vout caused by iron particles ranging from 50μm to 75μm and 125μm copper particles (a) Channel 1……………………………..…………………...……… 111 (b) Channel 2, (c) Channel 3…………………..……..………………. 112 (d) Channel 4……………………………..…………..………………. 113

6.10 Calculated relative inductance change caused by iron particles ranging from 50 μm to 75 μm and 125 μm copper particles (a) Channel 1, (b) Channel 2…………………..……………...……… 115 (c) Channel 3, (d) Channel 4…………………..……..………………. 116

7.1 Schematic of the integrated ultrasonic-inductive oil debris sensor………………………………………………………...... 127

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CHAPTER I

INTRODUCTION

1.1 Introduction

New technology for health monitoring in rotating machinery has been extensively sought by both military and civil industries for the last decade. Rotating equipments used by the military often seek to operate at the peak capacity to ensure accomplishment of the mission goals. As a result, even a small undetectable fault in the gear or bearing system can quickly develop into a dangerous failure mode. For commercial applications, efficiency, cost, and safety of operations are usually the primary goals. Unnecessary maintenance and work interruption are highly undesirable in their operations. Therefore condition based monitoring has become essential in maintaining and extending the health of high-speed rotating and reciprocating machinery. The real-time detection of machine component wear can eliminate the need for costly machine shutdowns for inspection, which would otherwise be required to avoid the possibility of catastrophic component failure during operation. One effective approach to detect signs of potential failure of a

1 rotating or reciprocating machine is to examine the life blood of a rotating or reciprocating machine: its lubrication oil.

A few studies [1, 2] have indicated that during normal machine operation, wear debris in lubrication oil is of constant concentration and small size, typically in the range of 1µm to 20µm; when abnormal wear begins, the debris concentration gradually increases, and the size of the debris particles grows as large as 50µm to 100µm. The concentration and size of particles increase gradually with time, until the machine fails.

Therefore, the ability to detect debris particles and determine their size is valuable in evaluating the level of wear in the machinery components. In addition, to reduce contact friction, tribological surfaces are often coated with special nonferrous coatings. Thus, the ability to differentiate ferrous and nonferrous debris can provide valuable information in identifying worn components and predicting the remaining life time.

A few real-time oil condition monitoring devices have been developed. Optical methods such as scattering counters are capable of detecting small particles in lubrication oils. However, the accuracy of the optical approach is affected by fluid clarity, particle refractive index and the existence of air bubbles [2, 3]. The acoustic emission detection method, based on the amplitude change of reflected acoustic waves, is sensitive to interference caused by background acoustic emission and lubrication oil temperature variation [2, 4]. Bulk capacitance sensing [5, 6, 7] uses a simple sensing structure, however the measured capacitance change often reflects not only the presence of particles but also changes in lubricant properties, such as total acid number and viscosity; this, in turn, creates difficulties in detecting debris. Furthermore, none of these methods can differentiate ferrous and nonferrous debris particles.

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1.2 Motivation

Predictive maintenance dependent on effective condition monitoring is essential in maintaining and extending the health of high speed rotating and reciprocating machinery used in many industries including aerospace, manufacturing and energy.

Real-time knowledge of the physical state of key mechanical components such as gears, bearings, and shafts, along with the ability to detect the changes in condition that are indicative of severe wear, are beneficial to the timely prevention of machines’ failures.

Accurate condition monitoring methodologies are being sought to determine the current health status of a machine, and facilitate the effective scheduling of maintenance and repair downtime [1-2]. Among them, one effective approach to detect signs of potential machine failure is by examining metallic wear debris in lubrication oil of a rotating or reciprocating machine.

Wear debris in lubricating oil are generated in three stages: break-in, normal wear, and abnormal wear. Examination of lubrication oil of the rotating or reciprocal mechanical parts has shown a direct relationship between the size and concentration of wear debris particles in the oil and the level of wear in the machinery components [2].

Thus by monitoring the size and concentrations of metallic debris in the lubricant, the condition of the rotating and reciprocal mechanical components can be determined.

A number of online and offline oil debris detection methods have been developed for identifying abnormal wear conditions [3-7]. The advantages and disadvantages of these methods have been reviewed in a recent article [8]. Among these methods,

3 inductive sensors have achieved certain successes in online metallic debris detection.

Because of their low cost and low maintenance cost, inductive sensors have been used for real time health monitoring of gear boxes and engines. In addition, inductive debris sensors can differentiate between ferrous and non-ferrous debris and have no response to air bubbles. Many inductive sensors using 3-D solenoids have been studied for oil debris detection, but they are limited to debris larger than 100 µm in size because of the low sensitivity of 3-D solenoids [8, 9, 10]. Thus, these devices are not sensitive enough to detect a problem before the onset of machine failure in many applications. In general, today’s online debris monitoring devices can provide only limited information on the progression of machine wear, and devices that provide more information do not have in situ online capability, and so cannot provide advance warning of sudden catastrophic failure.

1.3 Research objectives

The main research objective of this project is to develop a high throughput wear debris sensor based on inductive Coulter counting principle for high speed detection of micro scale metallic wear debris (less than 100µm ) in lubrication oil. Specific objectives are described in detail in the following sections

Part I Study of inductive Coulter counting principle and implementation of a microfluidic wear debris sensor

4

 Study the feasibility of the inductive Coulter counting method for detecting

metallic wear debris particles one by one and discriminating ferrous and non-

ferrous wear debris.

 Fabricate a single channel microfluidic device using soft lithography

micromachining.

 Establish an electrical model of the planar coil and obtain the relationship

between inductance change of the planar coil and particle size.

 Use copper and iron particles to test the single channel device and measure their

sizes.

 Analyze the effect of excitation frequency on particle size measurement.

Part II Implementation of an oil debris sensor using a meso-scale spiral coil to improve the throughput

 Study and compare the sensitivity of 3D solenoid and planar coil.

 Fabricate an oil debris sensor that used a two-layer spiral coil with a meso-scale

fluidic pipe crossing the planar coil’s center as the sensing element.

 Study a meso-scale sensor and demonstrate the feasibility of detection of wear

debris with significantly improved throughput using this sensor.

Part III Implementation of a multichannel sensor

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 Extend the inductive Coulter counting concept to a 7-channel device to detect

wear debris.

 Determine the electrical equivalent circuit of the multi-channel measurement

circuit.

 Analyze the crosstalk between individual sensing channels.

 Process the signal to obtain inductance change caused by presence of wear debris.

Part IV Implementation of a high-throughput, high-sensitivity sensor four channels sensor based on the resonance frequency division mechanism

 Design and build a measurement circuit based on the resonance frequency

division mechanism.

 Validate the feasibility of the sensing method by conducting simulation based on

equivalent circuit.

 Process the data from DAQ system to recover the inductance change caused by

metal wear debris in four channels.

 Conduct the experiment to detect and differentiate ferrous and non ferrous wear

debris in four parallel sensing channels by using only one set of measurement

circuit

1.4 Summary

The rest of this dissertation is arranged as the following: In Chapter II, a background and literature review on different methods used for wear debris detection are

6 presented. Next, a microfluidic device that uses the inductive Coulter counting principle for detection, counting and differentiation of metal debris particles in lubrication oil is presented In Chapter III. In the following Chapter, to overcome the limitations of micro scale device, a meso-scale oil debris sensor using a two-layer planar coil as sensing element is present. In Chapter V, to further improve the throughput, we designed and tested an inductive Coulter counter with seven detection channels to detect and count metal particles in lubrication oil. Crosstalk among channels was analyzed. Next, a proof- of-principle resonant frequency division multiplexed four-channel inductive sensor for detecting microscale metallic debris in lubrication oil using only one set of detection electronics is presented in Chapter VI. Finally, the conclusions are presented and future improvements are discussed in Chapter VII.

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CHAPTER II

BACKGROUND AND LITERATURE REVIEW

Currently, scheduled off-line laboratory analysis of oil samples including ferrography [11-12] and spectroscopy [13-16] is still the dominant technique for oil condition monitoring. For off-line analysis, oil samples are collected and sent to laboratories for analysis. Although ferrography and spectroscopy analysis at laboratories provides comprehensive and detailed information while other sensors or methods can only provide part of the information, the test procedures are time consuming, and require complicated setup and a skilled analyst. In addition, they hardly provide real-time information about machine health. In this paper, we present major advancements of on- line wear debris detection, i.e., a portion of the lubricant oil flow with debris is continuously analyzed when the machine is in operation; online wear detection allows continuous and automatic monitoring of wear debris in lubrication oil and thus provide real-time information about machine health condition. These advancements include sensors and instruments based on electrical impedance detection, acoustic detection, optical detection, pressure detection, online X-ray spectrography, resonant frequency

8 detection and electrostatic charge detection. For each detection method, the merits and limitations are presented and discussed.

2.1 Electrical impedance detection

When wear debris is present in lubricating oil, the electrical properties such as conductivity, permittivity and permeability of oil are affected and thus cause impedance change. By monitoring the impedance change of the lubricating oil, the presence, size and concentration of wear debris can be obtained. Due to its simple structure, low cost and low maintenance, this electrical impedance detection principle has been widely used for metallic wear debris detection.

2.1.1Resistive detection

Metallic debris has much higher conductivity than lubricating oils. When metallic debris particles are generated in lubricating oil, the resistivity of the oil will decrease.

Thus metallic debris can be monitored by measuring oil resistance. A typical sensor was developed by Shoji Itomi et al. [17] to monitor iron debris in automotive transmission and engine oil. The sensing element consists of a permanent magnet, and a layer of conducting material (carbon resin or conductive ceramic material) covering the end surface of the magnet, which is used as a sensing resistor. When the sensor is immersed in oil, ferrous debris are attracted by the permanent magnet and stick to the end surface of the sensing resistor, causing a resistance change of the sensing resistor. By monitoring

9 the resistance change of the sensing resistor, it is possible to detect the amount of ferrous debris in oil. However, due to temperature change in the lubricating oil, a thermal stress and thus a resistance change will be induced in the thin-layer sensing resistor, which in turn will causes difficulties in determining oil contamination.

Later on, the inventor improved the sensor design [18]. Figure 2.1 shows a schematic of the oil condition sensor consisting of a ring-shaped permanent magnet, a cup-shaped common electrode covering the end surface of the magnet, and eight rod electrodes positioned radically around the permanent magnet. All rod electrodes are axially spaced from the common electrode with different gaps. Debris in the oil accumulates in the gap between the common electrode and the rod conductors because of magnetic attraction force. When a sufficient amount of debris has accumulated in a gap, the rod electrodes will be electrically connected to the common electrode and induce a current. The wider the gap, the larger the amount of debris needed to induce a current. By counting the number of the rod electrodes that are electrically connected to the common electrode, it is possible to judge the oil contamination level by ferrous metallic debris. To circumvent the influence of oil temperature on resistance change, the inventor designed an electric circuit to compensate the temperature variation [19].

Resistive oil debris sensors are simple in structure and easy to use. However, both sensors rely on bulk measurement of a large number of ferrous debris accumulated over a period of time, therefore they are unable to provide real-time information of particle size distribution and concentration, which is critical to identify the worn components and predict the remaining life of rotating machinery. Additionally, the above sensors are only sensitive to ferrous debris.

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Figure 2.1 Schematic of a resistive oil condition sensor using eight rod electrodes

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2.1.2 Capacitive detection

Because lubricating oils are non-conductive, it is difficult to detect individual metallic debris particles in lubricating oils using a resistive detection method. To overcome this problem, capacitance detection has been used instead to detect individual metallic debris. A microfluidic device (illustrated in Figure 2.2) was developed to detect metallic debris by monitoring the change in capacitance across a pair of microelectrodes in a microfluidic channel [20, 21]. When 10 to 25 µm metal particles suspended in lubricating oil passed through the microchannel, capacitance pulses were detected owing to the difference in permittivity between the lubricating oil and the metal particle, and to the effect that the metal particle has on the electric field between the electrodes (Figure

3.3). Each pulse represents passage of metal debris. While the device can detect very small metallic debris particles (~10 µm), no noticeable difference in capacitance change was found among particles with same size but of different types of metal, indicating the sensor is unable to differentiate ferrous and nonferrous metal debris particles.

Additionally, because the difference in permittivity between dielectric debris and lubricating oils is small, dielectric particles present in lubricating oils are not likely to generate detectable capacitance changes and hence cannot be detected. We also note here that while an air bubble would not generate a capacitive pulse becasue the relative permittivity of air (εr=1.0) is close to that of oil (εr∼2.1–2.4), a water droplet would generate a capacitive pulse because the relative permittivity of water (εr=80) is much larger than oil. This may cause difficulties in differentiating water droplets from metallic debris.

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Figure 2.2 Schematic of a capacitive oil debris sensor [20]

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Figure 2.3 Measured capacitance change of the microfluidic sensor for lubricating oil

with 10-25 µm aluminum particles [20].

2.1.3 Inductive detection

Differentiation of ferrous and non-ferrous wear debris, which is important for condition monitoring of rotating and reciprocating machinery, can be enabled by inductive detection. The general sensing principle of inductive wear debris sensor is depending on two factors; magnetic permeability and eddy current (see Figure 2.4 [22]).

If a nonferrous metallic particle is present in the lubricating oil, an eddy current is generated inside the metal particle in a way that opposes the original magnetic field; as a result, the total magnetic flux is decreased, leading to a decrease in the sensor coil’s inductance. The higher the frequency of the AC excitation, the larger the eddy current

14 and therefore the larger the drop in the inductance. If a ferrous and metallic particle (with relative magnetic permeability µr significantly higher than that of lubricating oils) is introduced into the sensing coil, two factors, magnetic permeability and eddy current, contribute to inductance change in competing ways. First, because ferrous particle has a higher permeability, the magnetic flux would be enhanced, causing an increase in inductance. On the other hand, an eddy current would also be generated inside the particle, causing a decrease in inductance. At low frequencies, the eddy current is small, and the impedance increase caused by the change in magnetic permeability is dominant; thus, passage of a ferrous metallic particle generates a positive inductive pulse. Therefore ferrous and non-ferrous debris can be differentiated by looking at pulse polarity at an appropriate frequency. So far a number of oil debris sensors have been developed for online detection of ferrous and non-ferrous wear debris in lubricating oils [8-10].

Figure 2.4 Sensing principle of the inductive oil debris sensor. (a) Magnetic field is

induced in solenoid owing to a current passing through solenoid. (b) Magnetic flux is

15

attenuated owing to eddy current generated in a conductive but non-ferrous particle. (c)

Magnetic flux is enhanced owing to high relative permeability but also attenuated owing

to eddy current in a ferrous and conductive particle [22]

Figure 2.5 Schematic of an inductive debris sensor to remove the disturbance by air

bubbles and water droplets

An inductive oil debris sensor usually monitors inductance change by measuring the voltage change across or the resonant frequency of the sensing coil. Theoretically air bubbles and water droplets are not expected to induce inductance changes [22] because 1) the permeability of air and water are similar to that of oils, and 2) their conductivity is much lower than that of metals so the eddy current effect is low. However, in practice, air bubbles and water droplets do cause a change in stray capacitance, which in turn results in a voltage change across the sensing coil. Such a voltage change may be identified as passage of a metallic particle and generate false alarm. To overcome this problem,

Whittington and Flynn proposed an improved wear debris sensor design [23] as shown in

16

Figure 2.5. Before fabricating a solenoid around the non-magnetic oil pipe, a thin layer non-magnetic metal foil, such as aluminum foil, was wrapped around the oil pipe with a separation gap. The foil was grounded, acting as a Faraday screen to circumvent the disturbance by air bubbles and water droplets in lubricating oil and to keep the stray capacitance stable. A resonant circuit was also constructed to monitoring resonant frequency change caused by metal debris.

Figure 2.6 Schematic of a metallic debris sensor system incorporating an oil debris sensor

and two pressure sensors

To estimate debris concentration, it is important to accurately measure the local oil flow rate at a position where the debris is detected. Nikkels et al described a metallic debris sensor system [24] incorporating an inductive wear debris sensor and two pressure sensors positioned across an orifice as shown in Figure 2.6. The inductive debris sensor is used to detect ferrous and non-ferrous wear debris in oil and the two pressure sensors are used to measure differential pressure across the orifice, from which the local oil flow rate can be calculated.

17

In summary, inductive sensors have achieved certain successes in online metallic debris detection. Due to low cost and low maintenance cost, inductive sensors have been used for real-time health monitoring of gear boxes and engines without dielectric surface coating. However, the major limitation for inductive debris sensors, as well as for resistive and capacitive oil debris sensors, is that they can only detect metallic debris but are not sensitive for non-conductive debris. Therefore they provide insufficient information for health status of mechanical components with dielectric coatings such as hybrid ceramic bearing systems used in turboshaft machinery.

Figure 2.7 Schematic of an ultrasonic oil debris monitoring systems using two ultrasonic

transducers

2.2 Ultrasonic detection

Oil debris can be detected using ultrasonic techniques based on an acoustic power scattering mechanism. Debris particles in an acoustic field scatter the incoming acoustic

18 wave, causing a reduction in the amplitude of the wave reaching the receiver. The amplitude attenuation is related to the size of the scattering debris [25]. A recent patent

[26] describes an ultrasonic debris sensor (Figure 2.7). Two ultrasonic transducers are positioned on opposite sides of an oil pipe. One transducer is used as a transmitter sending ultrasonic wave, and the other transducer receives the ultrasonic wave.

Attenuation of the ultrasonic wave amplitude in the receiver indicates the size and number of wear debris. One limitation of this device is that it is unable to discriminate air bubbles from debris particles because air bubbles and wear debris particles cause similar acoustic attenuation.

19

Figure 2.8 Schematic of a pulse-echo ultrasonic oil debris sensor using a single ultrasonic

transducer, (a) Measurement setup, (b) Incident echo signals

Edmonds et al [2] reported an oil debris sensor that can differentiate air bubbles and debris particles based on difference in acoustic impedance. Figure 2.8 shows the schematic of the oil debris sensor. The sensor utilizes a focused pulse-echo ultrasonic

20 transducer acting as both ultrasonic emitter and receiver (Figure 2.8(a)). The incident acoustic pulse first generates an echo when it encounters debris particle, and then generates a large echo reflected from the back wall (Figure 2.8(b)). A debris particle can be identified because a debris pulse always exists for fixed time duration between incident pulse and back wall pulse. Because air bubbles have negative reflection coefficient, while solid particles have positive reflection coefficient in oil, air bubbles reflect the incoming acoustic pulse inverted, and the solid particles reflect the pulse non- inverted; therefore differentiation can be made by measuring the polarity or phase angle of the reflected acoustic echoes. This method has achieved success in detecting small debris particles (~3µm) and in discriminating air bubbles from debris particles. Because it responds to nonmetallic particles and has high resolution, the ultrasonic sensing method has been used for debris monitoring for aircraft propulsion systems.

One major problem for ultrasonic detection is that metallic debris and non metallic debris with same sizes generate similar acoustic signals in an ultrasonic field; thus metallic debris cannot be differentiated from dielectric debris. Additionally, a focused ultrasonic wave is usually used to detect individual debris particles and to improve the sensitivity. However, because the focused acoustic field is non-uniform, debris not in the central focal zone are either not detected or severely underestimated in size [2]. This may lead to large errors in reported debris size and concentration.

21

Figure 2.9 Schematic of an optical wear debris sensor based on light transmission

measurement

2.3 Optical and imaging detection

The light transmission property of lubricating oil is affected by the presence of oil debris. Thus debris contamination can be monitored by measuring light transmission.

Kwon et al propose a wear debris sensor to qualitatively detect wear debris by measuring the light density attenuation after light has transmitted through a lubricating oil sample

[27]. As shown in Figure 2.9, the sensor comprises an oil chamber, one inlet, one outlet, a light emitting diode to generate incident light, a photodiode to detect light intensity after light transmits through oil sample, and a solenoid coil to align ferrous wear debris. The light intensity of unused oil, J1, is first measured as a reference. Due to presence of wear

22 debris in lubricating oil, the light intensity reduces to J2. Thus the reduction from J2 to J1 is an indication of overall debris contamination of lubricating oil. Next, the solenoid coil is powered on to focus ferrous particles at two lines parallel to the chamber axis where the magnetic field gradient is the maximum; as a result the light intensity is increased to

J3. The difference between J3 and J2 is related to the contamination level by ferrous debris.

This method provides only qualitative and general information about oil contamination and fails to provide valuable information about debris size, shape and concentration, which are important to evaluate machine condition, identify mechanical faults, and alert the user of possible machine failure.

Figure 2.10 Illustration of wear debris sensors based on light blocking or light scattering

Debris suspended in transparent lubricating oil can be detected using light blocking or light scattering methods [28]. Figure 2.10 illustrates the sensing mechanism

23 of these types of oil debris sensors. When an incident laser beam encounters a suspended particle in the lubricating oil, the light beam is scattered, causing attenuation in the intensity of the light received by the blocking detector. The attenuation can be correlated to particle size. Additionally the scattered light intensity at different angles is also indicative of debris’ size and shape. Thus by measuring the light intensity attenuation of incident light beam, or measuring light intensity of scattered light, it is possible to detect individual wear debris suspended in the lubricating oil. However, the accuracy of this optical approach is affected by clarity of the oil, existence of air bubbles and particle properties (refractive index, shape, etc) [28]. Furthermore, it is unable to differentiate solid debris particles.

Figure 2.11 Illustration of a wear debris imaging system

A wear debris imaging system is demonstrated by Sebok et al [32] to measure the shape and size of wear debris. Figure 2.11 shows the schematic of the debris analysis

24 system. The sensor system consists of a laser generator, a flow cell, and an imaging device. As lubricating oil is pumped through the flow cell, a laser beam is generated to illuminate the flow cell at one side; the imaging device consecutively takes images at the other side. Detailed information about individual debris’ size and shape can be obtained by image processing; this information is critical to identify mechanical faults and worn components. Xie’s group [30, 31] has demonstrated a similar wear debris imaging systems. In the system, an electromagnet is used to aggregate wear debris flowing through an oil flow channel; a CMOS image sensor is used to obtain images of aggregated wear debris under illumination conditions. However, lubricating oils may become non-transparent after being used for a while before abnormal wear begins; this may cause difficulties for the imaging device to take clear images for health analysis of a rotary machine. Although a thin flow cell can be utilized to alleviate this problem [29], i.e., oil is forced to flow through a thin channel with a height of approximately 100 µm and images are taken of the thin oil film, doing so would lead to low throughput; the sensor is only able to analyze a small amount of oils at one time. Additionally, the sensor system requires expensive and complex image capturing and processing hardware and time-consuming pattern recognition algorithms, which limit its application for real-time oil debris applications.

2.4 X-ray spectrometer

Spectrographic analysis is one of the most prevalent oil analysis methods used in laboratory, providing comprehensive and detailed information about the oil sample.

25

However, traditional spectrography cannot be used for online oil analysis and cannot detect debris larger than 5 µm [32]. A recent patent proposed an off-line debris analysis method, utilizing filters to collect abnormal debris in an oil pipe line and X-ray spectrography to analyze larger abnormal debris [33]. Recently Spectro Analytical

Instruments Inc proposed a X-ray fluorescence (XRF) system for online detection of abnormal wear debris in lubricating oils [34]. Figure 2.12 shows the working mechanism of the wear debris detection system. When a wear debris particle passes through the flow cell, it is excited by the incident X-ray through the aperture and the flow cell window, and then emits a characteristic X-ray, which are detected by a X-ray detector. This method can measure particle contamination less than 250 ppm solids by weight with a flow rate of 3L/min. Although the X-ray spectrometer can provide more detailed information than electrical impedance detection and ultrasonic detection, it requires a sophisticated analysis instrument and a skillful analyst, making the detection system less practical for applications that require automatic monitoring of oil debris and low maintenance cost.

26

Figure 2.12 Schematic diagram of a debris detection system on X-ray spectrography

2.5 Summary

Bulk detection methods including resistive detection, resonant frequency detection, light transmission detection, pressure detection, and electrostatic charge detection rely on measurement of the bulk properties of oil sample that may contain many debris particles. Therefore they cannot provide real-time information of particle size distribution and concentration, which is critical to identify the worn components and predict the remaining life of rotating machinery. Oil debris sensors based on imaging and

X-ray spectrography can provide comprehensive and detailed information about oil debris such as content and shapes; however, their application for online oil monitoring is limited by the use of complex detectors and complicated data processing. Optical detection methods based on light blocking and light scattering can count debris and

27 measure sizes, but the accuracy is affected by the clarity of oils, existence of air bubbles and the particle’s refractive index.

So far inductive sensors have achieved certain success and show promise for real- time oil debris monitoring. Inductive debris sensors can differentiate ferrous and non- ferrous debris but are unable to detect metallic particles smaller than 100 µm . To decrease the detect limits to 50 µm , we propose to use a planar coil as the sensing element to detection microscale metallic debris in lubrication oil because a planar coil has higher sensitivity than a 3-D solenoid with the same amount of coil turns. An important advantage of the inductive sensor is that both air bubbles and water droplets, usually generated during machine operation, are not expected to generate inductance pulses with this device as proved in our prior work [11]. This is because 1) the permeability of air and water are similar to that of oils, and 2) their conductivity is much lower than that of metals so the eddy current effect is low. Therefore this sensor is expected to eliminate the false positives due to the presence of air bubble and water droplets that other oil debris sensing methods such as acoustic detection may encounter; this is for identifying faults and predicting the health status of rotary or reciprocating machinery.

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CHAPTER III

PRELIMINARY RESULTS: DEMONSTRATION OF INDUCTIVE COULTER

COUNTING PRINCIPLE USING MICROFLUIDIC CHANNEL DEVICE

3.1 Inductive Coulter counting principle

Figure 3.1 illustrates the sensing principle of the inductive Coulter counting device. An AC voltage is applied across the planar coil, and induces a magnetic field in the inductor (Figure 3.1(a)). If a ferrous but nonconductive particle (with relative magnetic permeability ur significantly higher than that of lubrication oil) is introduced into the microchannel, the magnetic flux would be enhanced (Figure 3.1(b)), causing an increase in inductance Ls. On the other hand, if a conductive but nonferrous particle is introduced into the microchannel, an eddy current is generated inside the metal particle in a way that opposes the original magnetic field (Figure 3.1(c)); as a result, the total magnetic flux is decreased, leading to a decrease in the inductance Ls. The higher the frequency of the AC excitation, the larger the eddy current and therefore the larger the drop in the inductance Ls.

29

Figure 3.1 Sensing principle of the inductive Coulter counting sensor. (a) Magnetic field

induced in planar coil owing to a current passing through solenoid, (b) Magnetic flux is

enhanced owing to high relative permeability particles, (c) Magnetic flux is attenuated

owing to eddy current generated in a conductive particle

When a particle is both ferrous and conductive, the two factors, magnetic permeability and eddy current, contribute to Ls in competing ways. At low frequencies, the eddy current is small, and the impedance increase caused by the change in magnetic permeability is dominant; thus, passage of a particle generates a positive voltage pulse.

For conductive but nonferrous particles at any frequency, the eddy current effect is dominant, and passage of a particle leads to an overall reduction in Ls. Therefore ferrous and non-ferrous debris can be differentiated by looking at pulse polarity at an appropriate frequency.

Assuming that wear debris are roughly spherical, the inductance changed caused by metallic wear debris is given by [8]

( ) (3.1)

30 in which Kco is a constant dependent upon coil geometry. For a coil with length equal to coil diameter, Kco is the inverse cubic of coil diameter. D is a complex function given by

( )  ⁄ [ (1 ) ] ⁄ (3. ) ( ) ⁄ [ (1 ) ] ⁄ where ( ) , is the angular frequency of the excitation signal, is the particle conductivity, is the permeability of particle and is the particle radius.

⁄ ( ) and ⁄ ( ) are modified Bessel functions of the first kind of order 1⁄ which are defined by

( ) ⁄ ( ) ( ) ∑ ⁄  ( 1. ) (3.3)

( ) ⁄ ( ) ( ) ∑ ⁄ ( . ) { where ( 1. ) and ( . ) are gamma functions [8]. The analysis presented here is restricted to solenoids with coil length equal to coil diameter. The inductance change of the solenoid caused by the introduction of iron and copper particles can be obtained by using Equations 3.1-3.3 and material parameters listed in Table 3.1.

Table 3.1 Material parameters of iron and copper

Material Iron 1.0×107 S/m 100 Copper 5.96×107 S/m 0.999994

31

(a) Inductance change caused by 150µm iron particles

(b) Inductance change caused by 150µm copper particles

Figure 3.2 Frequency response of a solenoid with diameter and length of 1mm

32

Figure 3.2 shows the frequency response of a solenoid for which length and diameter are both 1mm. The excitation frequency is swept from 100 kHz to 10 MHz.

Positive inductance changes are caused by iron particles and negative inductance changes are caused by copper particles. For both particles, the inductance change drops when excitation frequency increases because at a higher excitation frequency, a larger eddy current is generated and therefore the larger the drop in the inductance Ls. Inductance changes are measured using an Agilent E4980A LCR meter, which has an upper frequency limit of 2MHz. Therefore, to obtain larger inductance change for copper particle, the excitation frequency in inductance measurements are conducted at 2MHz.

(a) Iron particles with sizes ranging from 50µm to 150µm

33

(b) Copper particles with sizes ranging from 50µm to 150µm

Figure 3.3 Inductance change of solenoid calculated at 2MHz excitation frequency

Figure 3.3 shows calculated inductance change of the same solenoid at a 2MHz excitation frequency. For iron particles ranging from 50µm to 150µm , the inductance changes are from 0.024% to 0.612%. Those inductance change generated by copper particles ranging from 50µm to 150µm are smaller, from -0.001% to -0.047%. A solenoid is built and tested to validate equation (3.1). The solenoid is built by winding 14 turns of

AWG40 copper wire around a glass tube with 1mm inner diameter and 1.2mm outer diameter. Table 4.2 lists the inductance change measured at a 2MHz excitation frequency; the measured values match the prediction by using equation (3.1) very well. The difference between experiments and prediction is caused by the parasitic capacitance between the turns of the solenoid coil.

34

Table 3.2 Inductance change measured in a solenoid with 1.2mm coil length and coil

diameter

Material Iron Copper Particle size 50µm -75µm 75µm -105µm 105µm -125µm range Inductance 0.03% to 0.06% 0.08% to 0.16% -0.02% to -0.05% change

3.2 Demonstration of inductive Coulter counting principal by using planar coil

Having demonstrated the principles of inductive Coulter counting, we now proceed to experiments using a microscale device to detect microscale metal particles in lubrication oil. Microscale coils are generally made in a planar configuration because of the difficulty of three-dimensional microscale fabrication. We used the planar coil of a

PL3225TTE4R7M thin film inductor chip (KOA SPEER Electronics, INC.) after using sandpaper to remove the protective covering of the coil. A schematic of the planar coil is shown in Figure 3.4. The planar coil was fabricated on a ferrite substrate. It consists of a

13-turn copper coil and a pair of connection pads; each coil turn has a line width of 43

µm. When a metal particle moves close to the top surface of the planar coil, an inductance change is expected because of the changes in magnetic permeability and eddy current. A ferrous particle is expected to cause a positive change in inductance while an aluminum particle is expected to cause a negative change of inductance.

Five metal particles (three iron particles and two aluminum particles) were used in testing. Their approximate sizes are summarized in Table 3.3. The 100 µm and 500 µm aluminum and iron particles are roughly cylindrical in shape; they were created by

35 cutting small lengths of thin metal wires. The 80 µm iron particle is irregular in shape; it was made by filing an iron piece and its maximum dimension was 80 µm as measured by a microscope.

Table 3.3 Metal particles used in testing the microscale inductive Coulter counting

concept (D: Diameter; L: Length)

Iron particles Aluminum particles 100µm (D), 100µm (L) 100µm (D), 100µm (L) 500µm (D), 500µm (L) 500µm (D), 500µm (L) 80µm (D), irregular shape

Figure 3.4 Schematic of the microscale planar coil used for metal particle detection

The microscale sensing assembly was formed by fixing the microscale planar coil to a glass slide using double-sided copper tape. A strip of 50µm thick single-sided 36 cellophane tape was used to cover the top of the planar coil; it served as both electrical insulation and a protection layer.

Figure 3.5 Testing setup for the microscale planar coil

Pseudo-dynamic testing was conducted to demonstrate the sensing concept using the microscale sensing assembly. The testing configuration is shown in Figure 3.5. The microscale sensing assembly was immersed in a Petri dish filled with SAE 5W-30 lubrication oil. Metal particles were fixed at the free end of a plastic fiber attached to a mechanical holder. The plastic fiber was chosen because testing showed that the fiber by itself caused negligible inductance change in the planar coil. Two precision stages were used to control the position of the particle. The first controlled the vertical distance z between the particle and the planar coil. A second precision stage was used to move the holder and thus the particle in discrete steps across the face of the coil. In this way, our

37 test set-up mimics particles passing over the planar coil surface in fluid flow through a microchannel.

For the first test, the distance z of the particle from the face of the planar coil was fixed at z=50µm; i.e., the particle was in direct contact with the cellophane tape.

Measurements are taken at steps of 200µm horizontally across the face of the coil along the line indicated in the figure, which is about 500µm above the center line of the coil; we define our coordinate system so that this movement is along the x axis. Because the coil is not perfectly symmetric, the magnetic field produced by the coil is also not symmetric; moving along different lines in the x-y plane produced differently shaped inductance pulses. This particular line of movement was chosen because it resulted in an inductance pulse with a well defined single peak. An Agilent E4980A precision LCR meter was connected to the connection pads of the planar coil to monitor the inductance change. In all experiments, the testing signal used for the LCR meter was a 1Vpp, 2MHz sine wave.

Note here that preliminary impedance measurements indicated that for the planar coil, the capacitance has a relatively insignificant effect on the overall impedance at 2

MHz. Therefore, the LCR meter was set up to assume that the coil consists of a pure inductance and a pure resistance in series, and the inductance reading that it reported was taken as the inductance of the planar coil. The measurement time was set to “short time”; for this setup, the response time of the inductance measurement is 5.8 milliseconds.

When there was no metal particle in the lubrication oil, the base inductance was measured to be 930nH.

38

For the first test, three iron particles and two aluminum particles were used. Their approximate sizes are listed in Table 3.3. Figure 3.6 shows the inductance pulse caused by all five particles as they were moved along the x-direction. Figure 3.7, which is on a different vertical scale than Figure 3.6, shows the inductance pulse caused by a 80µm iron particle and a 100µm aluminum particle as they were moved along the x-direction, compared to the baseline inductance when no particle was present in the oil. The iron particles caused positive pulses in inductance because the increase in permeability is dominant; the aluminum particles induced negative pulses owing to the eddy current effect. The pulse width is comparable to the width of the planar coil chip, which is

3.2mm. The pulse heights caused by the 500µm, 100µm and 80µm iron particles correspond to inductance changes of 1.5%, 0.2% and 0.08%, respectively. The pulse heights caused by the 500µm and 100µm aluminum particles correspond to inductance changes of -1.0% and -0.025%, respectively. All inductance changes were detectable.

The testing results implied that the planar coil can be used to detect and differentiate ferrous and nonferrous microscale particles as small as 80 µm, with a pulse height for a given material that is related to particle size. The results are highly promising in that they suggest that by using smaller microscale planar coils with denser coil turns fabricated by micromachining, even smaller metal debris particles could be measured and differentiated.

39

Figure 3.6 Measured relative inductance change caused by iron and aluminum particles

Figure 3.7 Measured relative inductance change caused by a 100µm aluminum particle

and an 80µm iron particle

40

A second experiment was conducted to study the influence of the vertical spacing between the particle and the planar coil on the measured inductance pulse. The 100µm iron particle was used for this experiment. The particle was initially in direct contact with the cellophane tape (z=50 µm). The distance along the z axis was increased in steps of 100µm. The results are shown in Figure 3.8. The plot shows that as the distance increases from 100µm to 550µm, the pulse height reduces from 0.20% to 0.02%. This is because the magnetic field strength drops as z increases; therefore inductance change caused by both the magnetic permeability and eddy current is reduced as well.

Figure 3.8 Measured relative inductance change caused by a 100µm iron particle at

different vertical distances z from the coil

41

The test of the microscale planar coil demonstrated the feasibility of using the inductive Coulter counting principle for detection of individual metal wear debris particles. A device based on this principle would use a microscale coil embedded in a microchannel; metal debris suspended in lubrication oil passing through the microchannel could be detected and differentiated. Because the pulse height is affected by the vertical distance from the coil along the z-axis, and the pulse height and shape are affected by the particular line of movement taken in the x-y plane, a device that can accurately estimate particle size in a microfluidic channel may require a stage to focus particles along a particular line of movement in the channel, at a well controlled vertical distance from the coil.

3.3 Micro fluid channel debris sensor

3.3.1 Device description and fabrication

Figure 3.9 shows a schematic of the microfluidic inductive Coulter counting device. It consists of a microscale coplanar coil assembled to a 1.5 mm thick glass slide, an inlet reservoir, an outlet reservoir, and a single fluidic channel with dimensions of

250μm(H)×500μm(W)×3500 μm(L) fabricated on the surface of the planar coil. We used the planar coil of a 3.2mm×2.5mm PL3225TTE4R7M (shown in Figure 3.4) thin film inductor chip (KOA SPEER Electronics, INC.) after using sandpaper to remove the protective covering of the chip. The coil consists of a 13 spiral turns of copper fabricated on a ferrous substrate, and a pair of connection pads on the backside of the chip; each coil turn has a line width of 43 µm.

42

Figure 3.9 Schematic of the microfluidic device for metal particle detection in lubrication

oil

An AC voltage is applied across the planar coil, inducing a magnetic field in the microchannel. When a metal particle passes through the micro channel, it causes a detectable change in the inductance of the coil, Ls. If a ferrous metal particle (with relative magnetic permeability µr significantly higher than that of the lubrication oil) is introduced into the micro channel, two factors would affect Ls. First, the magnetic flux is enhanced due to the higher permeability µr, causing an increase in Ls. Second, an eddy current is generated inside the metal particle producing a magnetic field that opposes the original magnetic field. Thus, the eddy current effect tends to decrease the magnetic flux, leading to a decrease in Ls. The two factors, magnetic permeability and eddy current, contribute to

Ls in competing ways. For ferrous particles, the magnetic permeability effect is dominant for a wide range of excitation frequencies, and Ls increases. On the other hand, for nonferrous metal particles, the eddy current effect is dominant for sufficiently high excitation frequencies, and Ls decreases. Therefore, ferrous and nonferrous debris can be

43 differentiated by looking at pulse polarity. For this device, an excitation frequency of

2MHz was determined appropriate for this differentiation.

The fabrication of the device required two stages, one for the coil-glass substrate assembly and one for the micro channel. To assemble the coplanar coil to the glass slide

(1in×3in), we first made a 4.2mm×3.5mm rectangular through hole in the center of the glass slide using a diamond coated drill bit. Next we placed the glass slide on a flat surface, and set the chip with the planar coil into the center of the hole with the coil facing down. A small amount of epoxy was carefully applied to the gaps between the coil chip and the hole from the backside of the coil chip to fix the coil chip to the glass substrate. The coil-glass assembly was then flipped over with the coil facing up.

The micro channel was fabricated using a soft lithography method. A negative photoresist, SU8-2075 (MicroChem Inc., USA) was spincoated onto a microscope glass slide at 1000 rpm to achieve a thickness of 250µm. Photolithography was used to define the mold pattern for the microchannel. Once the desired pattern was obtained on the glass slide, PDMS (Sylgard 184, Dow Corning, USA) was poured over the mold and cured to transfer the pattern onto the PDMS. The PDMS layer was then carefully peeled off from the mold. Inlet and outlet reservoirs were formed by punching 3mm through holes in the PDMS layer. Next, the PDMS layer was attached to a 25μm thick cellophane tape with the microchannel facing down, to enclose the microchannel.

The device was completed by clamping the PDMS microchannel onto the coil- glass slide assembly. The microchannel was positioned in such a way that particles flowing through it passed across the face of the coil. The tape served as both electrical insulation and a protection layer for the coil.

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3.3.2 Experimental results and discussion

Iron particles and copper particles were used to test the device. Their approximate sizes are summarized in Table 3.4. Iron particles were obtained from iron powder (ChemicalStore.com, USA). Three brass sieves (mesh 140, mesh 200 and mesh

270, W.S. TYLER, USA) were used to separate the iron particles into two size ranges, one from 50µm to 75µm and the other from 75µm to 105µm. The copper particles were roughly cylindrical in shape; they were created by cutting small lengths of thin metal wires, and were measured by a microscope.

Table 3.4 Metal particles used in testing the microscale device

Iron particles Copper particles Iron particle 1 (75-105µm) Copper particle 1 (125µm) Iron particle 2 (50-75µm) Copper particle 2 (100µm)

For all experiments, 10mg of metal particles mixed with 10ml of SAE-5W30 lubrication oil were loaded into the inlet reservoir and forced to flow through the microchannel using a syringe. The flow rate is estimated to be 0.5ml/min. The inductance change of the coil was continuously monitored using an Agilent E4980A precision LCR meter that was connected to a computer (Figure 3.9). In all experiments, the excitation signal, which was produced by the LCR meter, was a 1Vpp, 2MHz sine wave. The response of the device was recorded. The base inductance of the device was measured to be 930nH when oil with no metal particles was loaded. Figure 3.10(a) and

Figure 3.10(b) show the testing results for iron particles with sizes from 75µm to 105µm

45 and from 50µm to 75µm, respectively. The results show that when oil with iron particles was loaded, positive inductive pulses were observed. Each pulse represents the passage of one particle through the microchannel. The inductance pulses generated by the larger iron particles (75 µm to 105 µm) range from 0.085% to 0.154%. The pulses generated by the smaller iron particles (50 µm to 75 µm) are smaller, ranging from 0.026% to 0.085%.

This shows that the inductance change is indicative of particle size. The observed pulse width is approximately 50ms.

Next we tested nonferrous copper particles of sizes of 100µm and 125µm, respectively; the results are shown in Figure 3.10(c) and Figure 3.10(d). When oil with nonferrous particles was loaded, negative inductive pulses were generated. The pulses generated by the 125µm particles are from -0.043% to -0.051%. Those generated by

105µm copper particles were smaller, from -0.024% to -0.029%. The slight variability in the sizes of the inductance pulses is because of 1) particle size differences due to the cutting method, and 2) differences in the z-direction positions of the particles passing the coil. A particle closer to the planar coil surface produces a larger pulse.

46

(a) Iron particles with sizes ranging from 75µm to105µm

(b) Iron particles with sizes ranging from 50µm to 75µm

47

(c) 125µm copper particles

(d) 100µm copper particles

Figure 3.10 Measured relative inductance change of the device for lubrication oil 48

Finally, 5mg of iron particles sized 75µm to 105µm and 5mg of 125µm copper particles were mixed with 10ml of SAE-5W30 lubrication oil for testing. Figure 3.11 shows the testing results. Both positive and negative pulses were generated; positive pulses were induced by iron particles, while negative pulses were induced by copper particles. Hence the device can differentiate ferrous and nonferrous metal particles.

Figure 3.11 Measured relative inductance change of the device for lubrication oil with

iron particles from 75µm to 105µm, and 125µm copper particles

A recent study [7] reported that during operation of rotary machines, debris particles of different shapes will be generated such as sphere-shaped, flakeshaped, and plate-shaped debris. The shape of a debris particle will affect the magnitude of the

49 inductive pulse. Miller and Kitaljevich [10] reported that the amplitude of the inductance pulse due to the passage of a metal debris particle through a solenoid wounded on a fluidic channel is proportional to the volume of the particle for ferrous particles and increases with the surface area of the particle for conductive but nonferrous particles.

However, the shape irregularity and the orientation of a particle inside the micro channel will also affect the eddy current and thus the magnitude of the inductance pulse. These effects will complicate the response of the sensor, which will be systematically studied in the future work. It is possible that the shape of the generated inductive pulse could give information about the shape of the particle, which should help in particle differentiation.

3.4 Summary

The experimental results described here demonstrate a microfluidic device that uses the inductive Coulter counting principle for detection, counting and differentiation of metal debris particles in lubrication oil. Unlike a bulk measurement method, the developed method produces output pulses with amplitudes correlated with the sizes of individual particles. The sensitivity can be improved by using smaller planar coils with denser coil turns. The detection of particles would not be affected by the change of oil properties. Note here that the throughput of this device can be significantly improved by using multiple microfluidic detection channels operating in parallel [35]. Thus this microfluidic device is promising for real-time detection of debris in lubrication oil for health monitoring of rotating and reciprocating machinery.

50

It is worthwhile to note here that for online measurement air bubbles and water bubbles may be present in pumped lubrication oils. However, air and water bubbles will not generate observable inductance changes when they pass the embedded sensing coil.

This is because the inductance change is due to two factors: (1) relative permeability (µr) difference between a particle and the oil, and (2) the eddy current generated in a conductive particle. First, the relative permeability of air and water (µr=1.0000037 and

0.9999992) are very close to that of lubrication oil (µr is approximately 1), thus the permeability effect is negligible. Second, the eddy current generated in an object is proportional to the conductivity of the material. The electrical conductivities of air and water (on the order of 10-14 and 10-2 S•m -1) are approximately 10-21 and 10-9 times lower than that of metal (on the order of 107 S•m-1). Hence the eddy current generated in air bubbles and water bubbles are negligible. Therefore, both air bubbles and water bubbles will not generate a detectable inductance change and cause false positives when they pass the embedded coil. In fact, in our experiments, we did observe the passage of air bubbles in fresh lubrication oil but no inductance pulse was detected.

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CHAPTER IV

DESIGN AND IMPLEMENTATION OF A MESO-SCALE WEAR DEBRIS SENSOR

In Chapter III, we fabricated a microfluidic inductive Coulter counting device that monitored the change in inductance of a planar coil embedded in the micro channel. The device can detect metallic particles as small as 50 µm owing to the inductive change caused by metal particles presented in micro channel. While this device has demonstrated using inductive Coulter counting principle for detection of small metallic particles, one drawback is that it used a micro fluidic channel (channel height 250μm) to force the particle to pass a planar coil very close to its surface, the most sensitive region of the planar coil. With such a small channel the sensor could analyze only a very limited amount of lubrication oil, and has a large chance of being clogged by large debris; therefore its capability for real time oil debris monitoring is limited. While the throughput can be increased by increasing the channel height, doing so would cause a decrease in sensitivity; a particle passing the coil far above the coil surface where the magnetic flux is weak may not be detected. More importantly, because the inductance change is very sensitive to particle’s position along the channel height direction, two debris particles with the same size but passing the coil at different vertical positions may cause different inductance changes; this in turns leads to errors in determining the debris size [36-39].

To overcome the above limitations, in this Chapter we demonstrate an oil debris sensor that uses a two-layer planar coil with a meso-scale fluidic pipe crossing the planar

52 coil’s center as the sensing element. Because of the use of a meso-scale fluidic channel, the throughput is significantly higher than the device using a microfluidic channel [11].

Debris particles are guided to pass through the center of the planar coil, the most sensitive region. Therefore the influence of particle’s vertical position is eliminated because the inductive pulses induced by all particles have the same inductance baseline [40].

4.1 Device design and measurement scheme

Figure 4.1 shows a schematic of the oil debris sensor. It consists of a two-layer planar coil assembled between two 1.5 mm thick glass slides, and a glass tube with a 1 mm inner diameter and a 1.2 mm outer diameter. To build the planar coil, first we drilled a 1.3 mm through hole at the center of each glass slide. Next two glass slides were clamped with a 175 µm gap, which was created by applying a 175µm spacing consisting of seven layers of cellophane tape at the upmost left and right edges of the two glass slides. Then the glass tube was inserted into the through hole; a small amount of epoxy was applied in the gap between the glass tube and the hole’s edge, and was dried in air to fix the glass tube. Next a 2 layer with ten turns on each layer planar coil was built layer by layer by carefully winding AWG 40 copper wire (80µm in diameter) around the glass tube in the 175 µm gap between the two glass slides. The major reason that we built a two-layer planar coil instead of using a 20-turn single layer coil is to enhance the magnetic flux density and inductance sensitivity at the central hole of the planar coil.

53

Figure 4.1 Schematic of the oil debris sensor for metal particle detection in lubrication oil

In comparison to the microfluidic inductive sensor presented in our prior publication [22], this sensor has two advantages. First, with the large central opening of the planar coil, it is able to use a relatively large fluidic pipe; hence the device can process a larger amount of lubricant per unit time. Secondly, a fluidic pipe perpendicular to the coil surface is used to guide debris passing through the center of the planar coil; this ensures each particle passes the most sensitive region and thus eliminates particle’s vertical position effect. This arrangement ensures that two debris particles with the same size cause the same inductance change when they pass the planar coil’s center through the fluidic pipe.

54

Figure 4.2 Illustration of sensing zones of inductive oil debris sensors using (a) a 3-D

solenoid and (b) a 2-layer planar coil as the sensing elements

4.2 Static test and analysis

In the debris sensor design we use a two-layer planar coil with dense coil turns as the sensing element to detect microscale metallic debris in lubrication oil because of its higher sensitivity than a 3-D solenoid with the same number of coil turns. As illustrated in

Figure 4.2, the sensing zone of a 3-D solenoid is the volume enclosed by the solenoids where the magnetic flux is uniformly distributed (see Figure 4.2(a)). In contrast, for a planar coil, the magnetic flux is concentrated near the surface of the planar coil’s center

(see Figure 4.2(b)), so its sensing zone is smaller than the sensing zone of a 3-D solenoid 55 with many coil turns. Outside of the sensing zone, the magnetic flux drops dramatically.

Because the change in magnetic flux and thus the change in inductance is proportional to the ratio of the volume of a debris to the volume of the sensing zone, the smaller the volume of the sensing zone, the higher the sensitivity. Therefore a debris passing through a planar coil causes a larger change in coil inductance than a debris passing through a 3-D solenoid.

To further increase the sensitivity, we used two layers of 10-turn coil instead of a

20-turn planar coil; this is because the outer 10 turns of a 20-turn planar coil are not effective in enhancing the magnetic flux density at the sensing zone. A static experiment was conducted to prove that the sensitivity of the two-layer planar coil is higher than a 3-

D solenoid with 20 coil turns; the 3-D solenoid for the experiment was built by winding

AWG 40 copper wires around a glass tube identical to the one used for fabricating the 2- layer planar coil. An 85µm in-diameter spherical iron particle was fixed at the free end of a plastic fiber attached to a precision stage. The plastic fiber by itself caused negligible inductance change. The precision stage was used to move the particle to the center of the

3-D solenoid and the center of the two-layer planar coil in discrete steps along the axis of the glass tube (z-direction). Both the 2-layer planar coil and the solenoid, including their glass tubes, were immersed in SAE-5W30 lubrication oil. Figure 4.3 shows the relative inductance change caused by the 85µm iron particle placed in both coils measured with an

Agilent 4980A precision LCR meter. It is obvious that the sensing zone of the 2-layer planar coil is narrower than that of the 3-D solenoid; the inductance change of the 2-layer planar coil drops dramatically as the particle was moved away from z=0 plane. In comparison, the inductance change of a 3-D solenoid drops much more slowly. In addition,

56 the relative inductance change measured at the center of the 2-layer planar coil is 0.095%, much higher than that measured at the center of the 3-D solenoid, 0.044%.

Figure 4.3 Measured inductance change caused by an 85µm iron particle as a function of

vertical position

Next, the relative magnetic flux density along radius direction (B(r)) of the two- layer planar coil on the z=0 plane along the radius direction was calculated using

Vizimag®, which performed a numerical integration using the theoretical equation of a current loop [37]:

    R 22rR  R r rR  B r B E sin 11K sin    0       ( .1)  R r   R r  R  r  R r  

57 where B0 is the magnetic flux density at r=0 on the z=0 plane, R is the radius of planar coil’s central opening, r is the radial position, E(θ) and K(θ) are complete elliptic integrals of the first kind and the second kind, respectively.

Figure 4.4 Relative magnetic flux density as a function of radial position at the central

plane (z=0) along radial direction

Figure 4.4 shows the calculated relative magnetic flux density (B(r)/B0) of the 2-D planar coil at z=0 plane. The maximum difference between B0 and B near the channel wall is 30%; because the inductance change is proportional to the volume of the particle, this variation will only cause an error up to 9% in particle size estimation. Hence two identical particles at different radial positions will generate similar inductance changes. This is

58 important for oil debris sensing because with a large fluidic pipe debris particles could appear in any radial position.

4.3 Experimental results and discussions

The experiment setup is illustrated in Figure 4.5. Metallic particles with known sizes were mixed with SAE-5W30 lubrication oils to test the device. A plastic tube was used to connect the syringe pump and inlet of the glass tube of the device. The oil sample with mixed metallic particles was pumped to pass the center of the planar coil by the syringe pump with a controlled flow rate, and was collected with a 1 liter oil tank. For all experiments the flow rate of oil sample was set to be 3ml/min. An Agilent E4980A precision LCR meter was connected to the planar coil to monitor the inductance change.

In all experiments, the testing signal used for the LCR meter was a 1Vpp, 2MHz sine wave. The LCR meter was set up to assume that the coil consists of a pure inductance and a pure resistance in series. The measurement time was set to “short time”; for this setup, the response time of the inductance measurement is 5.8 milliseconds. When there was no metal particle in the lubrication oil, the base inductance was measured to be 1.288µH.

59

Figure 4.5 Illustration of experimental setup

A series of experiments was conducted to test the device, using iron and copper particles. Their approximate sizes are summarized in Table 3.4. For each experiment, 1 mg metal particles mixed with 10 ml SAE-5W30 lubrication oil were loaded to the syringe pump and forced to flow through the fluidic pipe via a syringe pump. Response of the device was recorded. Figure 4.6(a) and Figure 4.6(b) shows the testing results for iron particles with sizes from 75 µm to 105 µm and from 50 µm to 75 µm, respectively.

Results show that when oil with iron particles was loaded, positive inductive pulses were observed. Each pulse represents the passage of one iron particle through the tube. The durations of the measured pulses range from 24 ms to 42 ms. The pulse heights generated by larger iron particles (75 µm to 105 µm) range from 0.091% to 0.167% and are larger than those generated by small iron particles (50 µm to 75 µm); the pulse heights for the

60 small iron particles ranged from 0.017% to 0.061%. This indicates the pulse height is indicative of particle size. Next we tested non-ferrous copper particles of sizes ranging from 105 µm to 150 µm ; the results are shown in Figure 4.6(c). It is obvious that when oil with non-ferrous particles was loaded, negative pulses were generated. The pulse height generated by copper particles (105 µm to 150 µm) is from -0.016% to -0.051%.

The difference in pulse height is primarily due to particle size difference.

We note here that with the same testing particles, the amplitude of inductive pulses measured with this sensor is similar to that with the microfluidic oil debris sensor

[22]. The flow rate was 3 ml/min, which is about 6 times higher than the flow rate used for the micro fluidic inductive oil debris sensor. Therefore our design using a 2-layer planar coil with a large central opening as the sensing element enables a high throughput oil debris sensor without sacrificing the sensitivity. Note here that the flow rate can be set higher to increase the throughput with a fast inductance measurement circuit; because of the slow response time of the LCR meter (5.8 milliseconds) in the testing a relatively low flow rate, 3ml/min, was set to ensure each inductive pulse was represented by at least

4 data points. Fewer data points may cause a large error in measured pulse amplitude and therefore an underestimation in particle size.

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(a) 75-105µm iron particles

(b) 50-75µm iron particles

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(c) 105-150µm copper particles

Figure 4.6 Measured relative inductance change caused by metal debris

Next 0.5 mg iron particles with diameters ranging from 50 µm to 75 µm and 0.5 mg copper particles ranging from 105 µm to 150µm mixed with 10 ml SAE-5W30 lubrication oil were tested. Figure 4.7 shows the testing results. Both positive and negative pulses were generated; positive pulses were induced by iron particles, while negative pulses were induced by copper particles. This test demonstrates the device can differentiate ferrous particles and non-ferrous particles.

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Figure 4.7 Measured relative inductance change caused by iron particles with size from

50 µm to 75 µm and copper particles with size ranging105 µm to 150 µm

We note here that detection of debris with diameters ranging from 20 μm to 50

μm is important, because it provides early warning for machine wear. While we only tested this device with 50 μm to 105 μm in-diameter iron particles and 105 μm to 150 μm in-diameter copper particles, the sensitivity of the device can be improved to detect smaller debris particles by using an external amplification circuit and noise shielding techniques to amplify the measurement signal. Additionally, smaller copper particles can be detected by using higher excitation frequencies; at a higher frequency, the eddy current effect is strengthened and causes a larger inductance change.

It is worthwhile to mention here that while the throughput can be further improved using a planar coil with a larger central opening to allow a larger flow rate,

64 doing so would cause a reduction in magnetic flux density in the sensing zone, resulting in a decrease in sensitivity. This may be compensated by adding additional layers of planar coils. However, if a large number of planar coil layers are added, the volume of the sensing zone would be increased and may cause a reduction in sensitivity. Hence there should be a limit for number of planar coil layers that can be added. To further improve the throughput without sacrificing the sensitivity, a device with multiple detection channels that are operated in parallel can be used to analyze a large volume of lubrication oil.

65

CHAPTER V

DESIGN AND IMPLEMENTATION OF A SEVEN CHANNEL MULTIPLEXED

WEAR DEBRIS SENSOR BASED ON UNDERSAMPLING TECHINIQUE

Chapter IV presented an oil debris sensor that used a two-layer planar coil and a meso-scale fluidic pipe. The use of a meso-scale fluidic channel allowed for much higher throughput than the microfluidic device. However, further improvements in throughput are needed to meet the requirements of online wear debris monitoring. To further improve the throughput without sacrificing the sensitivity, here we present a multichannel oil debris sensor based on the inductive Coulter counting principle with parallel detection fluidic channels to analyze a large volume of lubrication oil. A multichannel device allows simultaneous detection of metallic debris through its multiple parallel channels and thus has a much higher throughput if a large number of sensing channels are used.

However, rapid, simultaneous measurements of the highly dynamic debris-induced inductance changes from all sensing channels become a challenge. An undersampling process was used to overcome the problem.

This chapter is arranged as the following: in section 1, we present the design concept and sensing mechanism of the multichannel oil debris sensor. Next, an undersampling data processing method is presented for rapid measurement of inductive changes without a need for an advanced data acquisition system with extremely high sampling rate. In the following section, we present the experimental setup and the

66 dynamic testing results that demonstrate the high throughput counting of metallic debris in the lubrication oil using the multichannel device. Finally the conclusions are presented

[41].

5.1 Device design and measurement setup

Figure 5.1 shows the design concept of the multichannel inductive Coulter counting oil debris sensor. It consists of seven parallel fluidic channel-planar coil assemblies (sensing elements). Each fluid channel has a two-layer planar coil wound around a glass tube with a 1 mm inner diameter and a 1.2 mm outer diameter.

Figure 5.1 Schematic of the multichannel oil debris sensor for metal debris detection in

lubrication oil

To build a fluidic channel-planar coil assembly, we first drilled a 1.3 mm through hole on a glass slide. Next this glass slide was clamped to another glass slide, with a

175µm gap between the two slides. The gap was created by applying a 175 µm spacing,

67 made of seven layers of cellophane tape, at the upmost left and right edges of the two glass slides. Then the glass tube was inserted into the through hole; a small amount of epoxy was applied in the gap between the glass tube and the hole’s edge, and was dried in air to fix the glass tube. Next a 2-layer coil (each layer is a 10-turn planar coil) was built layer by layer by carefully winding AWG 40 copper wire (80µm in diameter) around the glass tube in the 175µm spacing. Finally, we assembled the seven fluidic channel-planar coil assemblies into the final device (Figure 5.1). A syringe pump, a common inlet pipe and a common outlet pipe were used to pump in and out the oil sample. Parallel sensing of oil debris via the seven sensing channels significantly improves the throughput.

Figure 5.2 Measurement circuit and equivalent circuit of the seven channel sensor

For debris measurements, the seven planar coils were electrically connected in series. Figure 5.2 illustrates the measurement setup and its equivalent circuit. Each planar coil is modeled as a series inductance Ls and a series resistance Rs. The sensing elements were serially connected to a sinusoidal excitation source, Vsin, and a sample resistor R.

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Figure 5.3 Illustration of waveforms of VL1 and VR

A NI USB-6251 DAQ (8 analog input channel, 16-bit) system was used to monitor the voltage to ground of each coil (V1 through V7) and the sampling resistor (VR).

The inductance of each coil can be calculated from the measured voltage traces. Taking

L1 as an example, Figure 5.3 illustrates the voltage waveform of V1 (blue line) and VR

(green line). VP1 and VPR are positive peak values of V1 and VR, respectively. T is the period of V1 and t1 is the phase difference between V1 and VR. Because L1 and R are connected in series, this results in

( ) ( .1)

where is the angular frequency of the excitation signal. In Equation 6.1, the left part and the right part are both complex functions; therefore they have equal phase angle and modulus:

69

( . )

{ √( ) ( ) where VP1 and VPR are positive peak values of V1 and VR, respectively. is the phase angle between V1 and VR which is defined by

휋푡 ( .3) 푇

By substituting equations 5.3 into 5.2, the series inductance L1 is obtained as:

휋푡 푇 푇 ( . ) 휋푡 휋 √1 ( ) 푇

For L2, first we calculate

휋푡 푇 푇 ( . ) 휋푡 휋 √1 ( ) 푇

Next we can obtain L2 by subtracting equation 5.4 from equation 5.5

휋푡 휋푡 푇 푇 푇 푇 ( .6) 휋푡 휋푡 휋 √1 ( ) 휋 √1 ( ) 푇 푇 where VP2 is positive peak values of V2 and t2 is the phase difference between V2 and VR.

Therefore, we can get Li (i=3,4..7) by calculating

휋푡 휋푡 푇 푇 푇 ( ) 푇 ( .7) 휋푡 휋푡 휋 √1 ( ) 휋 √1 ( ) 푇 푇 in which ti-1 is the phase difference between Vi-1 and VR, ti is the phase difference between

Vi and VR, respectively. From the above analysis, to measure the inductance change

70 caused by debris particles, we monitored the voltage to ground of each inductors and the sampling resistor (V1, V2 ..V7 and VR). The inductance changes were then calculated from the voltage traces using Equation 5.4 and Equation 5.7.

5.2 Undersampling measurement

(a1) Tdelay =0 (a2) Tdelay =0.6Texcitation

(b1) Tdelay =0 (b2) Tdelay =0.6Texcitation

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(c1) Tdelay =0 (c2) Tdelay =0.6Texcitation

Figure 5.4 Process and results of undersampling

According to Nyquist sampling theorem, to sample a 2 MHz sinusoidal wave, a minimum sampling rate of 4MHz is required [38]. However, a data acquisition system with such a high sampling frequency is not easily available. As an example, the sampling rate for NI’s high-end 8-channel, 16-bit USB 6251 DAQ system is 1.25 MHz.

Furthermore, if we use this DAQ card to take measurements of eight channels, the sampling rate for each channel should be 1.25MHz/8=156.25kHz, which is too slow to sample a 2 MHz sinusoidal wave. Here, an undersampling data acquisition method can be utilized to detect the voltage peaks and phase difference from sinusoidal shaped VR and

V1 to V7. Fundamentals of the undersampling data acquisition method can be found in

[42-44]. In brief, a high frequency sinusoid signal can be undersampled to obtain the peak values using a much lower sampling frequency. Figure 5.4 illustrates the undersampling process and the results of undersampling when different undersampling time intervals

Tsampling and different Tdelay (time lag between undersampling process and excitation signal) are used. In the Figure 5.4, Texcitation is the period of the excitation signal. A lower sampling frequency (1/Tsampling) can be used to undersample the excitation signal with an

72 undersampling time interval of Tsampling. Because the undersampling frequency is less than excitation frequency, Tsampling satisfies:

푇 푇 ( 1) 푇 ( .8) where n is an integer and . Figure 5.4 (a) shows the undersampling results when

푇 푇 푇 ( .9)

in which k is an integer and . In Figure 5.4 (a), we take n=1 and k=1, so that undersampling process is synchronized with the excitation signal (Tdelay =0); in this case, it is obvious that the undersamping process with a Tsampling defined in Equation (5.9) can capture the peak of the excitation signal (blue trace in Figure 5.4(a1)). However, a small

Tdelay will be generated when the initiation of undersampling process has a time lag, which will cause the undersampling process to miss the peak of excitation signal (blue trace in Figure 5.4(a2) with Tdelay =0.6Texcitaion).

Figure 5.4(b) illustrates undersampling process and the results when

푇 푇 푇 ( .1 )

Figure 5.4(b) shows that the undersampling process with 푇 1. 푇 specifically. The undersampling results indicate that undersampling frequencies which satisfy Equation (5.10) can capture the positive peaks and negative peaks of excitation signals. The undersampling result is not affected by Tdelay and follows a sine wave with a frequency fpeak (define as 1/Tpeak), where Tpeak is defined by

푇 푇 푇 ( .11) 푇 푇

Figure 5.4 (c) illustrates the undersampling process and the result when

73

푇 푇 푇 푇 { ( .1 ) 푇 푇 푇

Figure 5.4 (c) shows that the undersampling process cannot continuously capture the peak value of excitation signal, which leading to difficult to accurately calculate the inductance change.

(a) Excitation signals

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(b) Results after undersampling

Figure 5.5 Illustration of phase difference before and after the undersampling process. VL1

and VR are the signals from channel 1 and sampling resistance before undersampling,

while VL1′ and VR′ are the signals after undersampling

Figure 5.5 (a) illustrates the signals from a sensing channel (channel 1) and the sampling resistance under an input excitation signal with a period of Texcitation when sampled well above the Nyquist frequency. The channel inductance, LS1, resulted in a phase angle difference, , between VL1 and VR that can be written as:

휋푡 ( .13) 푇

Figure 5.5 (b) shows the same two signals undersampled with an undersampling interval

Tsampling (Tsampling=Tk+n*Texcitation, 0

휋푇 ( .1 ) 푇

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The phase time difference T1 between VL1′ and VR′ is:

푡 푇 푇 ( .1 ) 푇

By substituting Equation (5.11) and (5.15) into Equation (5.14), we can obtain

푇 푡 푇 휋푡 휋 ( .16) 푇 푇 푇 푇

The above derivation proves that β is equal to the phase angle difference ; indicates that the undersampling process does not cause a change in phase difference between VLi and VR. A numerical analysis was also conducted by using a lower frequency

(ranging from 10 kHz to 1 MHz with 1 kHz step size) that satisfied Equation (5.8) to undersample two voltage signals (2 Vpp, 1 MHz sinusoid signals) with a phase angle difference of 0.2π. Undersampling results confirmed that phase angle differences were

0.2π after undersampling processes. For a 2MHz sine wave excitation signal that will be used in most of the measurements, the undersampling frequency (defined as

) should satisfy Equation (5.10), which can be rewritten as

1 1 ( .17) . in which n is an integer larger than 0. The undersampling result will follow a sine wave with frequency fpeak; the fpeak can be obtained by combining Equation (5.11) and Equation

(5.17) as

8 1 ( .18) 1

Therefore, to obtain the peak of excitation signal at a high frequency, a smaller n is preferred. However, a small n will cause the high undersampling frequency which may exceed the sampling frequency of DAQ system. In the experiments, a NI USB-6251

76

DAQ system was used to undersampling the VR and V1 to V7. The highest sampling frequency for each channel is 156.25Hz. Therefore we can obtain n>12 from equation

5.16; some sampling frequency ranges satisfying Equation 5.10 are listed in Table 5.1.

Table 5.1 Undersampling frequency ranges

n 13 14 15 16 f sampling 150.9-153.8 140.4-142.9 131.1-133.3 123.1-125 (kHz) N 17 18 19 20 f sampling 115.9-117.6 109.6-111.1 103.9-105.3 98.8-100 (kHz)

Figure 5.6 Illustration of calculating the standard deviation of peak values

Any digital noise generated by the function generator used to produce the excitation signal and the DAQ card used to collect data will cause a variation of peak values. Therefore, experiments were conducted to determine the undersampling frequency that is optimal in the sense that it measures peak values with the least variation.

The process is presented in Figure 5.6. First, a 1ms of excitation signal (1 Vpp, 2 MHz sin wave) was generated from an Agilent 33220A function generator, and was then undersampled by the NI USB 6251 DAQ card; the positive peak and negative peak were

77 detected using an undersampling frequency ranges listed in Table 5.1. The standard deviation of the captured peak values is:

1 √ ∑( ̅ ̅̅ ̅) ( .19)

where VPi is the peak values obtained in Figure 5.5 and ̅̅̅ ̅ is the average of VPi

Figure 5.7 Standard deviation of voltage peak

Figure 5.7 shows the standard deviation of peak detection with undersampling frequency sweeping from 100 kHz to 157 kHz with a step of 1 kHz. It can be seen that minimum standard deviation occurs at 111 kHz (blue dot in Figure 5.7). Additionally,

111 kHz falls in the frequency ranges from 109.6 kHz to 111.1 kHz, which means its

78 satisfies the Equation 5.10 with n=18. Then the 111 kHz is chosen as the undersampling frequency to sampling the 2MHz excitation signal for least digital noise. By using the

111 kHz sampling rate, we can monitor the sine wave-shaped voltage trace at a frequency of 11935 Hz (period is 83 μs). In other word, using the undersampling method, we can capture the peak value of voltage or inductance at a frequency of 11935 Hz. Assuming five to ten data points are needed to accurately represent one inductance pulse/voltage pulse, using this method, we can detect a voltage/inductance pulse with a pulse width no less than 415 µs to 830 µs.

5.3 Signal processing

Nevertheless, the use of the undersampling technique for peak measurements of a high-frequency sine wave signal will induce certain errors in capturing the peak values.

As shown in Figure 5.4(b), after using the undersampling technique the result is an irregular sinusoidal wave, which will cause errors to detect peak values. To reduce the error, we used cubic spline to interpolate the undersampling results. As shown in Figure

5.7, red line is the undersampling results of a 2 Vpp, 2 MHz excitation signal by using a

111 kHz sampling rate. Next the undersampling results are processed using the cubic spline interpolation by using code written in Matlab. The blue line shown in Figure 5.7 indicates that the positive peak values were recovered after using the cubic spline interpolation method.

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Figure 5.8 Interpolation of VR obtained from under sampling

5.4 Crosstalk analysis

One challenge for using multichannel device is the crosstalk among the channels because all sensing inductors are electrically connected in series. When particles pass through one channel and generate a voltage pulse it may cause false detection at other channels due to the crosstalk. However, analysis showed that for the multichannel inductive sensing device the use of serial electrical measurement circuit (shown in Figure

5.2) led to a reduction in crosstalk.

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Figure 5.9 Crosstalk between channel 1 and channel 2

To analyze the crosstalk, first the equivalent electrical circuit shown in Figure 5.2 was simulated in Cadence® Pspice® to obtain VR and V1 to V7. Here we used the

Lsi=900nH, Rsi=1Ω, R=10Ω; all values are close to measured values of the device. Next, the data was imported to Matlab® to calculate the coil inductances. For the simulation, we induced an inductance change (1% and 10%) in channel 1 and calculated the inductance change in channel 2 and other channels at different frequencies ranging from

1kHz to 6MHz. Figure 5.9 shows the crosstalk between channel 1 and channel 2. It indicates that the crosstalk in channel 2 is negligible (less than 0.001). Figure 5.10 shows the simulation results of relative inductance changes at 2 MHz in channel 1, by inducing

81 a 1% inductance change in channel 1. As shown in Figure 5.10, there is no detectable crosstalk in channel 2.

Figure 5.10 Inductance change of channel 1 and channel 2

5.5 Experimental results and discussions

To validate the undersampling method for inductance measurement (presented in section 5.1 to section 5.3), a single detection cell of the 7-channel device (shown in

Figure 5.1) was used for demonstration. A 105μm iron particle was forced to pass through fludic channel of the detection cell. The Agilent E4980A LCR meter and NI

USB 6251 DAQ system were used to monitoring the inductance change of channel 1 caused by this particle separately. Figure 5.11(a) and Figure 5.11(b) shows the inductance

82 measurement results using LCR meter and undersampling method, respectively. As shown in the Figures 5.11, the magnitude of the inductance pulse obtained by NI USB

6251 DAQ system matched well with the result obtained by the Agilent E4980A LCR meter. The pulse width obtained by the undersampling method is slightly larger than that obtained by the LCR meter; this is caused by different flow velocity. This test demonstrated the validity of the undersampling method for inductance measurement.

(a) LCR meter

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(b) DAQ system

Figure 5.11 Inductance change caused by a 105um iron particle

Copper and iron particles suspended in SAE 5W-30 motor oil was used to test the device. The oil sample with mixed metallic particles was pumped to pass the center of the planar coil by the syringe pump with a controlled flow rate, and was collected with an oil tank. For all experiments the flow rate of oil sample was set to be 21ml/min. An Agilent

33220A Function Generator was connected to seven planar coils and sampling resister Rs.

NI USB-6251 DAQ system was used to monitor the voltage change across inductors and sampling resistor. In all experiments, the testing signal used from the Function Generator was a 10Vpp, 2.01MHz sine wave. The DAQ system was set up to 111 kHz sampling rate to undersampling VL1 through VL7 and VR.

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Figure 5.12 shows the testing results. 1 mg iron particles with diameters ranging from 50 µm to 75 µm and 1 mg copper particles ranging from 125 µm to 150µm mixed with 10 ml SAE-5W30 lubrication oil were tested. Both positive and negative pulses were generated; positive pulses were induced by iron particles, while negative pulses were induced by copper particles. The pulse heights generated by iron particles (50 µm to

75 µm) ranging from 0.042% to 0.071%. The pulse height generated by copper particles

(125 µm to 150 µm) is from -0.031% to -0.051%. The difference in pulse height is primarily due to particle size difference and the cross-talk between each channel is negligible. This test demonstrates the device can differentiate ferrous particles and non- ferrous particles. The high throughput was attained by using seven fluidic sensing channels in parallel. Compared to the single channel sensor, the throughput has been improved seven times without sacrificing the sensitivity. An undersampling data acquisition method was used to rapidly measure the inductance pulses from multiple detection channels without using a data acquisition system with an extremely high sampling rate.

85

Figure 5.12 Measured relative inductance change caused by iron particles with size from

75 µm to 105 µm and copper particles with size ranging125 µm to 150 µm

86

Note here that the measured relative inductance changes by iron particles (50 µm to 75 µm) and copper particles (125 µm to 150 µm) using the multichannel sensor are very similar to those using a single channel sensor with the same 2-layer planar coil and the sensing channel reported in Chapter V. This further proves the validity of the multichannel measurement scheme and undersmapling data processing for debris-induced dynamic inductance changes.

Although these pulses shown in Figure 5.12 seem like crosstalk as they occur at a similar moment in time, we conducted further experiments to prove they are very unlikely crosstalk signals. The following pseudo-dynamic tests were conducted to confirm that these pulses were caused by debris particles’ passing though parallel sensing channels at a similar time. In the tests, a 105 µm -in-diameter iron particle was fixed at the free end of a thin plastic fiber. The fiber with iron debris attached was then manually moved in and out of Channel 3 and Channel 6 using a precision stage. A NI USB-6251

DAQ system was used to monitor the voltage change across the sensing coil and the sampling resistor. The inductance changes were then calculated from the voltage traces using Equation 5.4 and Equation 5.7. The results are plotted in Figures 5.13(a) and (b).

Figure 5.13(a) shows that when the iron particle passed through Channel 3, there was no detectable crosstalk in its adjacent channels, Channel 2 and Channel 4. Figure 5.13(b) shows that when the iron particles passed through Channel 6, there was no detectable crosstalk in its adjacent channels, Channel 5 and Channel 7. Note that the particle used here is 105 μm, and is expected to cause a much larger inductive pulse that the 50-75 μm particles in the 7-channel experiments. If there had been crosstalk in the 7-channel experiments, there would be large and obvious crosstalk generated in the pseudo-dynamic

87 experiments using larger iron particles. Hence we conclude that the pulses listed in previous table were not crosstalk; they were caused by iron debris passing through parallel channels at a similar time moment. Figure 5.13 and corresponding texts were added to my thesis.

(a) Debris passing through Channel 3

88

(b) Debris passing through Channel 6 Figure 5.13 Measured relative inductance change caused by a 105 µm iron particles

To prove there is negligible crosstalk among sensing channels, next a cross correlation analysis was performed between the signals from two sensing channels at a time. A cross correlation analysis was performed between the signals from two sensing channels (shown in Figure 5.12) at a time. The result shows all cross correlation coefficients |r| is less than 0.02, indicating that there is negligible correlation among the pulses of different channels. This confirmed seven sensing channels were able to simultaneously detect and count oil debris with negligible crosstalk among channels.

5.6 Summary

89

We demonstrated a high throughput inductive sensor for detecting micro scale metallic debris in nonconductive lubrication oil. The high throughput was attained by using seven fluidic sensing channels in parallel. Compared to the single channel sensor, the throughput has been improved 7 times without sacrificing the sensitivity. An undersampling data acquisition method was used to rapidly measure the inductance pulses from multiple detection channels without using a data acquisition system with an extremely high sampling rate. Cross correlation analysis indicated that crosstalk among channels is negligible. The multichannel oil debris sensing method can be extended to a large number of detection channels to obtain a very high throughput.

90

CHAPTER VI

IMPLEMENTATION OF A FOUR CHANNEL MULTIPLEXED WEAR DEBRIS

SENSOR BASED ON RESONANCE FREQUENCY DIVISION TECHNIQUE

Compared to the single channel sensor, the throughput presented in previous

Chapter has been improved 7 times without sacrificing the sensitivity. Under sampling data acquisition method was utilized to rapidly measure the inductance pulses from multiple detection channels without using a data acquisition system with an extremely high sampling rate. Cross correlation analysis indicated that crosstalk among channels is negligible. However, the number of channels is restricted by DAQ system because for an

N-channel device, a DAQ system with N+1 analog input channels is required. If N is large, such a DAQ system would be impractical.

To overcome this limitation, here we present a four channel multiplexed wear debris sensor based on parallel LRC resonant circuit. The advantageous of this four channel sensor is it eliminates needs for a DAQ system with large number of analog data acquisition channels. Unlike sensing method used in Chapter VI, only a single DAQ channel is used which allows us to increase the number of sensing channels without being restricted by the DAQ system. Additionally, only one single DAQ channel is required, a higher sampling frequency can be used to improve the throughput and sensitivity.

The rest of this chapter is arranged as the following: in first section, we present the design concept and measurement setup of the multiplexed multichannel oil debris

91 sensor. Next, parallel resonant method is presented to detect inductive changes in multi sensing channels using only one DAQ channel. In the following section, simulation was conducted based on equivalent circuit to validate the parallel resonant sensing method.

Next we present the pseudo-dynamic and dynamic testing results that demonstrate the high throughput counting of metallic debris in multichannel using only one DAQ channel.

Finally the conclusions are presented.

6.1. Device design and measurement setup

6.1.1 Device design and resonant frequency division multiplexing concept

Figure 6.1 illustrates the design concept of a four-channel multiplexed inductive

Coulter counting oil debris sensor. It consists of four parallel fluidic channel-planar coil assemblies (sensing elements). Each sensing element consists of a meso-scale fluidic channel (1mm in inner diameter) made of glass and a two-layer planar coil wrapped around the fluidic channel. Each sensing coil is electrically connected in parallel with an external capacitor Cpi (i=1, 2, 3, 4) to form a parallel LC resonant circuit that has a unique resonant frequency. The fabrication process of fluidic channel-planar coil assembly was described in our prior publication [9]. Once the assembled final device (see Figure 6.1) was assembled, a syringe pump was used to load the oil sample the multichannel sensor via a common inlet pipe.

92

Figure 6.1 Schematic of the multiplexed four-channel oil debris sensor for metal debris

detection in lubrication oil

Figure 6.2 Measurement setup and equivalent circuit of the four channel debris sensor

For multiplexed oil debris detection, the four planar coils were electrically connected in serial. Figure 6.2 illustrates measurement setup and equivalent circuit of the four channel debris sensor. Each planar coil is modeled as an inductance Lsi in series with a resistance Rsi (i=1,2,3,4). Cpi (i=1,2,3,4) is the external capacitor, which is connected to each planar coil in parallel to form a parallel LC circuit. The four sensing elements were serially connected to a sinusoidal excitation source, V0, with an internal resistor R0.

93

Parallel LC resonance mechanism was applied to the multichannel oil debris detection.

First, specific Cpi for each sensing channel was selected so that each sensing coil had a unique resonant frequency. Second, a combined excitation signal (VSIN) that consists of four sine waves whose frequencies are close to the resonant frequencies of the four sensing channels was applied, and only one combined response Vout was measured.

Because each sensing channel only exhibits a peak amplitude at its resonant frequency, the signals for each individual channel can be recovered from the combined response by taking the spectrum components at each resonant frequency. Inductance change for each channel can therefore calculated from individual signals. The signal processing signal is discussed in section 6.1.3.

6.1.2 Measurement parameter determination

For each sensing coil used in the sensor, Lbase-i is approximately 800nH and Rsi is approximately 0.9 Ω, measured from a Agilent E4980A precision LCR meter. At the frequency range the excitation frequency (fi) used in the measurement (1.5MHz to 2.5

MHz), the reactance of Lbase-i, Xi = 2흅fiLbase-i, is much larger than Rsi. Therefore the resonance frequency of this parallel LC circuit is approximately determined by [10]:

1 (6.1) 휋√ 𝑏 𝐶

At the resonant frequency, impedance of the parallel LC circuit reaches a peak value while the phase angle is zero. By adjusting the value of parallel capacitor Cpi, the resonant frequency of each individual sensing channel can be regulated differently. For a

94 sensing coil with an inductance of Lbase-i to have a specific resonant frequency (fresonant-i), the external capacitance can be determined by

1 𝐶 (6. ) (휋 )

(a)

95

(b) Figure 6.3(a) Relative impedance change of the parallel LRC circuit caused by coil inductance change at different excitation frequency, fi. Here Lbase-i=800nH, Cp=7.91nF,

Rs=0.9Ω, resonant frequency = 2MHz), (b) Relative impedance change of the LRC

circuit as a function of relative inductance change (re-plotted from the data shown in

Figure 6.3(a))

Next we will determine excitation frequencies for each sensing coil/channel.

Figure 6.3(a) shows the relative impedance change (ΔZ/Z) of one coil, simulated as a parallel LC resonant circuit, as a function of coil inductance change at three different excitation frequencies. Figure 6.3(b) plotted the relative impedance change of the LRC circuit as a function of relative inductance change using the data shown in Figure 6.3(a).

Note that when a ferrous metallic particle passes through the coil, it generates a positive

96 inductive change; while a nonferrous metallic particle generates a negative inductive change [9]. We expect at a selected excitation frequency fi, ferrous and non-ferrous debris can be differentiated by the impedance pulse polarity. However, as shown in Figure 3, if the excitation frequency, fi, is equal to the resonant frequency,

(6.3)

either a positive inductance change caused by a ferrous debris or a negative inductance change caused by a nonferrous debris induces a negative impedance change (the black curve in Figure 6.3(a) and 6.3(b)), making it impossible to differentiate ferrous and nonferrous debris. This problem can be eliminated by shifting the excitation frequency fi slightly away from the resonant frequency of the parallel LC circuit. If the excitation frequency is set to be slightly lower than the resonant frequency (e.g., 1.9MHz),

(6. ) the passage of a ferrous particle will generate a positive impedance change, while a non- ferrous particle will generate a negative impedance change (the red curve in Figure 6.3(a) and 6.3(b)). Alternatively, if the excitation frequency is slightly higher than the resonant frequency (e.g, 2.1 MHz),

(6. ) the passage of ferrous particle will generate a negative impedance change, while a non- ferrous particle will generate a positive impedance change (the blue curve in Figure 6.3(a) and 6.3(b)). Therefore by shifting the excitation frequency slightly away from the resonant frequency, ferrous and non-ferrous debris can still be differentiated by looking at the pulse polarity of the LC parallel impedance. As long as the excitation frequency fi is close to the resonant frequency fresonant-i, signals from each individual channel can still

97 be recovered from the combined response by taking the spectrum components at each resonant frequency.

6.1.3 Signal processing

A combined excitation signal (VSIN) consisting of four sine waves with the selected measurement frequencies was applied to the multichannel sensor. The output voltage Vout was monitored and recorded by a 14-bit Gage RazorTM Digitizer at a 100MHz sampling rate. Then Fast Fourier Transform was conducted for Vout to obtain the voltage components at excitation frequencies (i.e, Vout(f1), Vout(f2), Vout(f3) and Vout(f4)). The impedance Zi for each sensing channel is

1 ( ) 𝑆 𝑆 𝐶 𝑍 𝑖 1, ,3, (6.6) 1 ( 𝑆 𝑆 ) 𝐶 in which is the a ngular frequency defined by

휋 𝑖 1, ,3, (6.7) where is the frequency components of the excitation signal. Next we can obtain Vout

𝑢 (6.8) where K is defined by

𝑍 𝑍 𝑍 𝑍4 ( , , , , 4) (6.9) 𝑍 𝑍 𝑍 𝑍4

Therefore we can calculate LS1 to LS4 from

( ) ( , , , , ) 𝑢 4 𝑢 ( ) ( , , , , 4) ∗ (6.1 ) 𝑢 ( ) ( , , , , 4) [ 𝑢 ( 4)] [ ( 4, , , , 4)]

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6.2 Simulation based on equivalent circuit

To validate the sensing method presented in previous section simulation was conducted based on the equivalent circuit shown in Figure 6.2. The parallel resonant sensing concept was extended to four sensing channels. The simulation parameters of the circuit are listed in Table 6.1. The frequency interval between the resonant frequencies of adjacent channel was set to 0.4 MHz to reduce the crosstalk among sensing channels. For each sensing channel, the excitation frequency is 0.05 MHz lower than its resonant frequency. The combined excitation signal used in simulation is shown in Figure 6.7, which is superimposed of four 2.5 Vpp sinusoid wave. The case inductance Ls of each channel is 800 nH. The series resistances Rs of each sensing coil is 0.5 Ω.

Table 6.1 Simulation parameters for channel 1 to channel 4

Channel 1 Channel 2 Channel 3 Channel 4

fresonant 1.4 MHz 1.8 MHz 2.2 MHz 2.6 MHz

fexcitation 1.35 MHz 1.75 MHz 2.15 MHz 2.55 MHz

Cp 16.2 nF 9.8 nF 6.5 nF 4.5 nF Inductance change 0.05 -0.05 -0.06 0.03 (%)

Next we simulated there are metal debris passing through four sensing channels simultaneously. The relative inductance changes induced by metal debris are listed in

® ® Table 6.1. Vout was simulated and recorded in Cadence Pspice . FFT was conducted for each 0.1ms duration of Vout data (5 cycles of combination wave). Because the sampling rate is 100MHz and the frequency of cubic spline interpolation is 1GHz, hence for each

99

0.1ms segment Vout data，there are 100k data points. The smallest power of 2 that is

17 17 greater than 100k is 2 . Therefore a 2 -point FFT was applied to Vout data, which resulted in a bin number of 216 (65536) and a bin width of 7.6 kHz. Flattop window was applied to reduce the frequency spectrum leakage and improve the amplitude accuracy [11]. Next experiments were conducted to confirm that the flattop window can recover peak values of

Vout(fi) with the least voltage variation. First a 1s duration of combination excitation signal was generated from an Agilent 33220A function generator, and was then monitored and recorded by Gage RazorTM Digitizer at a 100MHz sampling rate. Then we used cubic spline to interpolate the recorded Vout at a 1GHz frequency. FFT was conducted for each

0.1ms duration of Vout data to obtain Vout(f1) to Vout(f4). The voltage variation of Vout(f1) to

Vout(f4) by using different FFT windows are shown in Table 6.2. The results show that the

Flattop FFT window generated the least voltage variation. Hence Flattop FFT window was selected for rest of experiments. The FFT and calculation of relative inductance change were implemented using code written in Matlab®.

Table 6.2 Base voltage variation of Vout(f1) to Vout(f4) by using different FFT windows

Variation (%) Window Vout(f1) Vout(f2) Vout(f3) Vout(f3)

Flattop ±0.0115 ±0.0100 ±0.0093 ±0.0081 Rect ±0.0434 ±0.0210 ±0.0803 ±0.0950 Hamming ±0.0220 ±0.0086 ±0.0417 ±0.0419 Hann ±0.0189 ±0.0071 ±0.0332 ±0.0330

100

As shown in Figure 6.4, for each individual sensing channel, the calculated relative inductance change (blue curve in Figure 6.4) matches well with the simulated relative inductance change (red curve in Figure 6.4), which means the multichannel debris sensor based on parallel resonant method is feasible of detecting and counting metal debris in four sensing channels simultaneously using only one single DAQ channel.

(a) Channel 1

101

(b) Channel 2

(c) Channel 3

102

(d) Channel 4

Figure 6.4 Setup and Simulated inductance change at channel 1 to channel 4

Figure 6.5 Measured waveform of Vout

103

6.3 Experimental results and discussions

6.3.1 Determination of excitation frequency by experiments

After assembling the final device as shown in Figure 6.1, parameters of the parallel resonant circuit, i.e., inductance and resistance of the sensing coil Lbase-i and Rsi were measured using an Agilent E4980A LCR meter. The measurement values were shown in Table 6.2. The target resonant frequencies for the four sensing channels were set to be 1.4MHz, 1.8MHz, 2.2MHz and 2.6MHz. We intended to set the frequency interval between the resonant frequencies of adjacent channels to be ~0.4MHz to reduce the possible crosstalk among sensing channels. Four commercially available external capacitors or capacitor combinations, 12.1nF, 9.1nF, 5.77nF, 5.2nF, were selected for the measurement. Next an Agilent 33220A function generator was connected to the multichannel device to measure the actual resonant frequencies. A sine wave with a 10V peak to peak amplitude and frequency sweeping from 1MHz to 3MHz with a step of

0.01MHz was generated from the function generator and applied to the device. Output

TM voltage Vout was measured and recorded (as shown in Figure 6.5) using a Gage Razor

CompuScope 14-bit Multi-channel Digitizer at a 100 MHz sampling rate. From the testing result, the actual resonant frequency of each parallel LC resonant circuit were obtained (1.37 MHz, 1.76 MHz, 2.20 MHz, and 2.69 MHz) and listed in Table 6.3.

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Table 6.3 Measured parameters to determined excitation frequencies for channel 1 to

channel 4

Channel 1 Channel 2 Channel 3 Channel 4

fresonant-i 1.37 MHz 1.76 MHz 2.20 MHz 2.69 MHz

Cp 12.1 nF 9.10 nF 5.77 nF 5.20 nF

Lbase-i 753 nH 782 nH 846 nH 720 nH

Rs 0.89 Ω 0.94 Ω 0.97 Ω 0.90 Ω

(a) Channel 1

105

(b) Channel 2

(c) Channel 3

106

(d) Channel 4

Figure 6.6 Frequency response of relative change of Vout for channel 1 to channel 4 as a function of excitation frequency fi when a 105 µm iron particle is introduced in individual

channel

As discussed in section 6.1.2, to be able to differentiate ferrous and non-ferrous metallic debris from impedance response, the excitation frequency of each sensing channel, fi, should be shifted slightly away from the resonant frequency, fresonant-i.

Additionally sensitivity of each sensing channel is also related to the excitation frequency utilized in measurement. To determine the optimal excitation frequencies with a maximum sensitivity, pseudo-dynamic testing was conducted by using a 105µm-in- diameter iron particle. The iron particle was fixed at the free end of a plastic fiber. The plastic fiber was chosen because testing showed that the fiber by itself caused negligible

107 inductance change in the sensing coil. The fiber with iron debris attached was pushed through each individual sensing coil by a precision stage. Next for each sensing coil the frequency response of Vout near its resonant frequency was recorded using the Gage

Digitizer at a 100MHz sampling frequency. The results of Δ(Vout)/Vout are plotted in

Figure 6.6. For channel 1 and channel 2, the highest Δ(Vout)/Vout or sensitivity occurred at the negative peak on the Δ(Vout)/Vout curves (Figures 6.6(a) and (b)); negative voltage pulses were induced by iron particles. For channel 3 and channel 4, the highest

Δ(Vout)/Vout or sensitivity occurred at the positive peak on the Δ(Vout)/Vout curves (Figures

6.6(c) and (d)); positive pulses were induced by iron particles. The excitation/measured frequencies for each parallel resonant circuit, selected from Figure 6.6, are listed in Table

6.4. In the following experiment, we used these four excitation frequencies for multiplexed debris detection measurements.

Table 6.4 Determined excitation frequencies for channel 1 to channel 4

Channel 1 Channel 2 Channel 3 Channel 4

fi 1.55MHz 1.90MHz 2.10MHz 2.55MHz

6.3.2 Dynamic testing results and discussions

Next, copper and iron particles suspended in SAE 5W-30 motor oil was used to test the resonant frequency division multiplexed 4-channel device. Iron particles were obtained from iron powder (ChemicalStore.com, USA). They are roughly spherical in shape. Two brass sieves (mesh 200 and mesh 270, W.S. TYLER, USA) were used to select the iron particles into from 50µm to 75µm. The copper particles were roughly

108 cylindrical in shape; they were created by cutting small lengths (125µm) of thin metal wires (125µm in diameter), and were measured under a microscope [9]. 1 mg iron particles with diameters ranging from 50 µm to 75 µm and 1 mg copper particles with

125 µm in diameter mixed with 10 ml SAE-5W30 lubrication oil were tested. The oil sample with mixed metallic particles was loaded to pass through the sensing coils via fluid channels by a syringe pump with a controlled flow rate, and was collected with an oil tank. For this experiment the flow rate of oil sample was set to be 12ml/min.

Figure 6.7 Combined excitation signal with four frequency components (1.55 MHz, 1.90

MHz, 2.10 MHz and 2.55 MHz)

109

Figure 6.8 Frequency spectrum of Vout

An Agilent 33220A Function Generator sent a combined excitation consisting of sinusoidal waves with four frequencies (1.55 MHz, 1.90 MHz, 2.10 MHz and 2.55 MHz) determined above, with 10V peak-to-peak amplitude, to the four channel debris sensor.

The waveform of the combined excitation signal is shown in Figure 6.7. The output voltage, Vout, was recorded by the 14-bit Digitizer at a 100MHz sampling rate. Next FFT was conducted for each 0.1ms duration of Vout data (5 cycles of combination wave). Flat top window was applied to reduce the frequency spectrum leakage and improve the amplitude accuracy [45]. Figure 6.8 illustrates the frequency spectrum of Vout data which duration is 0.1ms. Then Vout(fi) shown in Figure 6.8 (frequency domain) can be utilized to represent the voltage peak (time domain) of four sinusoidal signals because the excitation

110 signal consists of four sinusoidal wave. The Vout(fi) at fi can be obtained by using peak detection code written in Matlab®.

(a) Channel 1

111

(b) Channel 2

(c) Channel 3

112

(d) Channel 4

Figure 6.9 Measured relative change o Vout caused by iron particles ranging from 50µm to

75µm and 125µm copper particles

Figure 6.9 shows the relative change of output voltage for channel 1 to channel 4.

For channel 1 and channel 2, because the excitation frequencies are higher than their resonant frequencies, positive pulses were induced by copper particles, while negative pulses were induced by iron particles. For channel 3 and channel 4, copper particles generated negative pulses and iron particles generated positive pulses because the excitation frequencies are lower than its resonant frequency. Worth mention that a few small crosstalk pulses were observed in channel 3 and channel 4 (red pulses in Figure

6.9). This is from Equations 6.8-6.10, the demodulated output voltage Vout(fi) of each individual channel is dependent on impedance of other sensing channels; an inductance

113 change in one channel is likely to cause a voltage change in a neighboring channel even if no debris particle passes through the neighboring channel. Nevertheless, this problem can be solved by calculating the relative inductance change using Equation 6.10. In the following we demonstrated that there was no false positive inductive pulses caused by the crosstalk in voltage change signals.

The voltage signals (Vout(fi)) after FFT were then used to calculate the relative inductance changes of each sensing channel from Equation 6.10. Note that Equation 6.10 is a nonlinear equation set, which makes it difficult to deliver an exact solution set. Instead iterative method was utilized to calculate LSi. First Equation 6.10 was rewritten as

푟( ) ( ) ( , , , , ) 𝑢 4 푟( ) ( ) ( , , , , ) 𝑢 4 (6.11) 푟( ) 𝑢 ( ) ( , , , , 4) {푟( 4) 𝑢 ( 4) ( 4, , , , 4) where r(fi) is the residual. For channel 1 to channel 4, the base inductance was listed in

Table 6.1. In each sensing channel, the relative inductance changes which caused by metal debris are ranging from -0.10% to +0.10%. Hence we swept LSi from 0.9%*Lbase-i to

1.1%*Lbase-i with a step size of 0.01% *Lbase-i. Termination criteria for the iterative procedure was specified as

푟( ) . 1𝑚 𝑖 1, 3,, (6.1 )

Then the approximations LS1, LS2, LS3 and LS4 of Equation 6.10 can be computed using codes written in Matlab®.

114

(a) Channel 1

(b) Channel 2

115

(c) Channel 3

(d) Channel 4

Figure 6.10 Calculated relative inductance change caused by iron particles ranging from

50µm to 75µm and 125µm copper particles

116

Table 6.5 Relative inductance change caused by iron particles and copper particles in

channel 1 to channel 4

Channel 1 Channel 2 Channel 3 Channel 4 0.032% 0.033% 0.031% 0.025% Iron particles to to to to 50µm -75µm 0.068% 0.056% 0.048% 0.046% -0.033 % -0.025% -0.084% -0.090% Copper particles to to to to 125µm -0.045% -0.040% -0.092% -0.110%

Figure 6.10 shows the calculated relative inductance changes for channel 1 to channel 4. The pulse heights generated by iron particles (50 µm to 75 µm) and copper particles (125µm) are listed in Table 6.5. In each channel, the difference in pulse height is primarily due to particle size difference. For all channels, positive pulses were induced by iron particles because for iron particles magnetic the permeability factor is dominant; negative pulses were induced by copper particles because for copper particles the eddy current effect is dominant. Channel 1 with an excitation frequency of 1.55MHz has the highest sensitivity for iron particles because eddy current effect is smaller at lower excitation frequency. Channel 4 with an excitation frequency of 2.55MHz has the highest sensitivity for nonferrous copper particle detection, because the higher the excitation frequency, the larger the eddy current and therefore the drop of LS. This test demonstrates using resonant frequency division multiplication; the device can simultaneously detect and differentiate ferrous and nonferrous metallic particles via a single set of detection electronics. The high throughput was attained by using four fluidic sensing channels in parallel. Compared to the multichannel channel device described in our prior work, only one single set of detection electronics is required to monitor four parallel sensing

117 channels, which makes it possible to extend this sensing concept to a high throughput debris sensor with large number of sensing channels.

A cross correlation analysis was also performed between the relative inductance change signals from two sensing channels (shown in Figure 6.10) at a time. The result shows all cross correlation coefficients |r| is less than 0.05, indicating that there is negligible correlation among the pulses of different channels. This confirmed four sensing channels were able to simultaneously detect and count metal debris with negligible crosstalk among channels.

Note here that in Figure 6.10, the measured relative inductance changes caused by iron particles (50 µm to 75 µm) and copper particles (125 µm) are different at different excitation frequencies. This is because the permeability effect and the eddy current effect on inductance change are dependent on the measurement frequency [7]. Nevertheless, within the same sensing channel (with the same excitation frequency), the difference in pulse height is primarily caused by particle size difference. For each specific sensing channel/sensing coil, calibration can be done to determine relations between pulse height and particle size under each measurement frequency; ferrous and nonferrous particles should be calibrated separately. Actual debris sizes in lubrication oil, critical for determining the health status of rotating and reciprocal mechanical components, can be obtained from the calibration curves.

It is worth mentioning here that although we demonstrated a wear debris sensor for wear debris detection in four parallel sensing channels simultaneously utilized only one set of measurement circuit, there is one limitation to extend this sensor to a device with a large number of sensing channels to further improve the throughput. To calculate the

118 relative inductance change of four channels, iterative method was utilized. For one single sensing coil, it takes 20 steps to sweep LSi from 0.9%*Lbase-i to 1.1%*Lbase-i. Hence for the four channels device presented in this paper, the maximum computation step is 204, making it is difficult to continuously detect debris for a long time period if the number of channels is large; hence the iterative algorithm needs to be improved to be able to rapidly compute relative inducance changes for real-time condition monitoring of mechanical components.

6.4 Summary

In this Chapter, we demonstrated the proof-of-concept of a resonant frequency division multiplexed four-channel inductive sensor for detecting microscale metallic debris in lubrication oil. Parallel LC resonance method was used to monitor the inductance change from multiple sensing channels using only one set of detection electronics. Only one combined excitation signal is needed and only one voltage output needs to be recorded, which greatly reduce the complexity of detection hardware. Using the resonance frequency division multiplexing method, inductance changes caused by debris particles from individual signals were successfully recovered. Compared to the multichannel sensor presented in our prior work [11], the signal-to-noise ratio has been significantly improved because the impedance change was amplified nearly 5 times by the resonance peak near the resonance frequency, making the measurement method possible to detect smaller metallic debris. The sensitivity can be further improved by optimizing the inductance and resistance of the sensing coil, and the parallel external capacitance. Cross correlation

119 analysis indicated that crosstalk among channels is negligible. While a 300% improvement was demonstrated using the 4-channel device, the resonant frequency division multiplexing sensing concept can be potentially extended to multichannel device with a large number of sensing channels to further improve the detection throughput.

120

CHAPTER VII

CONCLUSIONS AND FUTURE WORK

7.1 Conclusions

A micro fluidic device based on an inductive Coulter counting principle to detect metal wear particles in lubrication oil is first presented. The device detects the passage of ferrous and nonferrous particles by monitoring the inductance change of an embedded coil. The device was tested using iron and copper particles ranging in size from 50 to 125

µm. The testing results have demonstrated that the device is capable of detecting and distinguishing ferrous and nonferrous metal particles in lubrication oil; such particles can be indicative of potential machine faults in rotating and reciprocating machinery.

The inductive coulter counting concept was then implemented using a meso scale debris sensor with a two-layer planar coil wound around a fluidic pipe. The device detects the passage of metallic debris by monitoring the inductance change of a two-layer planar coil with a meso-scale fluidic pipe crossing its center, which is designed to attain high throughput without sacrificing the sensitivity. The testing results using iron and copper particles ranging in size from 50 to 150μm have demonstrated that the device is

121 capable of detecting and distinguishing ferrous and nonferrous metallic debris in lubrication oil with a high throughput.

Then the sensing concept was then extended to a multichannel device; a high throughput was achieved by parallel detection of oil debris via seven fluidic sensing channels. Sensing element is for individual channel is a two-layer planar coil with a meso-scale fluidic pipe crossing the coil’s center. Because of the small volume of the sensing zone, the device has a high sensitivity and is able to measure metal particles as small as 50 µm. The high throughput has been achieved by using parallel detection channels which allows parallel counting particles pass through its multiple channels while still requiring only one single AC source. Compared to the single channel sensor, the throughput has been improved 6 times without sacrificing sensitivity. However, this device requires monitoring individual voltage output from each sensing channel; if a large number of sensing channels are used, it becomes difficult to monitor signals from all channels individually because 1) implementation of detection electronics is complex and impractical for online condition monitoring, and 2) the amount of data that needs to be acquired becomes impractically large.

To overcome this limitation, we present the use of resonant frequency division multiplexing to the signals from multiple sensing channels of a four-channel inductive pulse sensor. Each sensing channel/coil is connected in parallel with an external capacitance and has a unique resonant frequency. Signals from individual channels are thus modulated differently. As a result, a multiplexed signal representing a number of channels is acquired by taking only one measurement. Next that signal can be demodulated to recover the individual channel signals. The use of signal multiplexing

122 techniques allows us to obtain signals from all individual sensing channels by taking measurement of only one combined signal. Thus a large number of sensing channels can be used for simultaneous analysis of a large volume of lubrication oil samples to achieve a very high throughput.

7.2 Future works

7.2.1 Improvement on detect limits of inductive debris sensor

It is worth mentioning that the use of LC resonance method improves the signal to noise ratio for debris detection. This is because near the resonance frequency, the change in impedance (and in output voltage) due to a small inductance change caused by passage of debris particle is amplified by the resonant peak. The sharper the resonant peak, the larger the amplification ratio. While in the current configuration we observed an approximate 5-time increase on output voltage change using the LC resonance method over the inductance measurement method that we used in prior publications [11]. Future work will be done to optimize the inductance and resistance of the sensing coil as well as the parallel external capacitance to further increase the signal-to-noise ratio for detecting even smaller debris.

The detection limit of the two-layer sensing coil can be further reduced to detect smaller metallic debris particles by using a planar coil with higher coil density and using a large-amplitude excitation signal with appropriate frequency. In addition, the sensor is suitable for measuring debris with relatively low concentrations in lubricants (e.g., in early stage of abnormal wear). Literature has suggested that at the early stage of

123 abnormal wear, debris concentration ranges from 40-80 particles/ml, which is similar to the concentration used in our tests, 0.1mg/ml. If debris concentration is very high, it is possible that two or more debris enter the sensing zone at the same time and induce a large-magnitude inductive pulse with a complex pulse shape. Pattern recognition algorithm may be needed to determine whether the pulse was caused by multiple debris particles or a large debris particle.

7.2.2 Integrated inductive-ultrasonic debris sensor

So far inductive and ultrasonic oil debris sensors have achieved certain success and show promise for real time oil debris monitoring. Inductive debris sensors can differentiate ferrous and non-ferrous debris but unable to detect dielectric debris; ultrasonic debris sensors respond to all wear debris with a high resolution but cannot differentiate solid wear debris. A combination of inductive detection and ultrasonic sensing, however, may lead to an oil debris monitoring system that can differentiate not only ferrous and nonferrous debris but also metallic and dielectric debris. Such a system is important for real time monitoring of rotary machines such as rotorcraft transmissions and turboshaft engines, and should be explored.

The acoustic detection method, based on the amplitude change of reflected acoustic waves, has been researched to detect solid wear debris. A typical acoustic oil debris device consists of two acoustic transducers facing each other, one as transmitter, and the other as receiver [10]. An acoustic beam is sent to the fluid from the transmitter.

When debris particle passes through the acoustic beam, it scatters the incoming acoustic

124 wave and causes a reduction in the amplitude of the wave reaching the receiver. While the transmitter-receiver measurement configuration can detect all solid debris, it cannot differentiate air bubbles generated in circulating lubricant oil during machine’s operation from solid debris. Recent work [11] indicated this problem can be overcome by measuring reflected acoustic pulse echoes produced by debris particles. The method uses an ultrasonic transducer as both transmitter and receiver to send a sequence of large- amplitude ultrasonic pulses to the lubrication oil. A particle passing through the acoustic field scatters the acoustic wave and produces a pulse echo. Particle size can be determined from the amplitude of the echoes. Neremiah et al. [11] reported that because air bubbles scatter an ultrasonic pulse uniformly in all directions while solid debris primarily backscatters the pulse, solid debris can be differentiated from air bubbles by measuring the scattering power at specified off-axis angles. However, this method requires the use of a couple of ultrasonic transducers. In addition, crosstalk among the transducers may cause false identification of particles. Although air bubbles and solid particles can be differentiated by spectral analysis because they have distinct spectral shape [12], this method requires sweeping a large frequency band when the particle is present in the acoustic field, and is not fast enough to detect all debris if flow rate or debris concentration is relatively high.

Recently Edmonds et al [2] reported a method that differentiates air bubbles and solid particles based on difference in acoustic impedance. Because air bubbles have negative reflection coefficients, while solid particles have positive reflection coefficients in oil, air bubbles reflect the incoming acoustic pulse inverted, and the solid particles reflect the pulse non-inverted; therefore differentiation can be made by measuring the

125 polarity or phase angle of the reflected acoustic echoes. While this method achieves certain success in discriminating air bubbles and solid particles, it has one major disadvantage: the focused acoustic field has a non-uniform Bessel shaped acoustic intensity profile; acoustic intensity reaches maximum at the center of the focal zone, and decreases to zero outside of the focal zone. Therefore large debris outside of the focal zone may produce a small echo, and hence may be counted as a small debris. Even worse is that, debris near the oil channel wall, far away from the focal zone, may not generate detectable echoes because of the weak incident acoustic intensity. Thereby debris not in the central focal zone are either not counted or severely underestimated in size; this will lead to large errors in measurement of debris size and concentration. In addition, an ultrasonic sensor responds to all solid debris; it is unable to differentiate between metallic debris and non-metallic debris.

To overcome the limitations of ultrasonic debris sensors, in our future work, we plan to present an integrated wear debris sensor that consists of 1) an ultrasonic pulse sensor with a novel flow recess structure, which ensures all solid wear debris (metallic and non-metallic) pass the acoustic focal zone, thus all debris can be counted and measured accurately, and 2) an inductive pulse sensor based on the proven inductive

Coulter counting technology [8, 9], which detects only metallic debris (ferrous and non- ferrous). By comparing responses of the inductive Coulter counting sensor and the ultrasonic pulse sensor non-metallic debris can be detected.

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Figure 7.1 Schematic of the integrated ultrasonic-inductive oil debris sensor

Figure 7.1 shows the schematic of the integrated oil debris sensor. The integrated sensor consists of 1) an inductive pulse sensor made of a two-layer planar coil, and 2) an ultrasonic pulse sensor utilizing a spherically focused ultrasonic transducer for producing a focused acoustic beam and receiving an acoustic echo generated by debris particle, 3) a housing block with a hole for accommodating the piezoelectric ultrasonic transducer, 4) an inlet channel and an outlet channel to load and collect the sample oil, and 5) a flow recess structure between the inlet channel and the outlet channel to create a measurement zone that is within the acoustic focal zone of the ultrasonic transducer.

Wear debris suspended in lubrication oil are pumped into the device through the inlet channel, and are guided to pass an inductive pulse sensor first. The inductive pulse sensor detects and counts all ferrous and non-ferrous metallic debris. Details of the inductive pulse sensor were described in our prior publication [9]. Debris particles are then guided to pass an ultrasonic pulse sensor. Debris particle present in the acoustic field

(generated by the ultrasonic transducer) scatters the incident acoustic beam and induces

127 an acoustic echo; the echo’s magnitude is related to the size of the debris. Because the spacing (S) between the inlet channel and the collection channel is short (< 2mm), the lateral diffusion of oil stream is negligible in the flow recess region. Thus the flow path is entirely within the acoustic focal zone at the center. The passage of debris through the central acoustic focal zone ensures accurate measurement of debris size. The ultrasonic pulse sensor detects and counts all solid debris (metallic and non-metallic), and distinguishes debris from air bubbles. Therfore the integrated sensor should have capability to detect and indentify ferrous and non-ferrous metallic debris and non- metallic debris.

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