Low-Cost, High-Frequency, Data Acquisition System for Condition Monitoring of Rotating Machinery Through Vibration Analysis-Case Study

sensors

Article Low-Cost, High-Frequency, Data Acquisition System for Condition Monitoring of Rotating Machinery through Vibration Analysis-Case Study

César Ricardo Soto-Ocampo * , José Manuel Mera, Juan David Cano-Moreno and José Luis Garcia-Bernardo Railway Technology Research Center (Centro de Investigación en Tecnología Ferroviaria-CITEF), Mechanical Engineering Department, Universidad Politecnica de Madrid, 2 José Gutiérrez Abascal Street, 28006 Madrid, Spain; [email protected] (J.M.M.); [email protected] (J.D.C.-M.); [email protected] (J.L.G.-B.) * Correspondence: [email protected]

Received: 26 May 2020; Accepted: 18 June 2020; Published: 20 June 2020

Abstract: Data acquisition is a crucial stage in the execution of condition monitoring (CM) of rotating machinery, by means of vibration analysis. However, the major challenge in the execution of this technique lies in the features of the recording equipment (accuracy, resolution, sampling frequency and number of channels) and the cost they represent. The present work proposes a low-cost data acquisition system, based on Raspberry-Pi, with a high sampling frequency capacity in the recording of up to three channels. To demonstrate the eﬀectiveness of the proposed data acquisition system, a case study is presented in which the vibrations registered in a bearing are analyzed for four degrees of failure.

Keywords: data acquisition system; low cost; high sampling rate; Raspberry; vibration test bench; bearing diagnosis; envelope analysis

1. Introduction The progressive increase in industrial development has generated an obvious competition among businesses, who are seeking to maximize production and minimize operating costs. This gives rise to the need for ensuring the proper operation of mechanical equipment by means of condition monitoring (CM). This requires a high degree of sensing, that must be eﬀective and depreciable in a short space of time, seeking to optimize the intervention timeline to allow the entire life cycle of each component to be exploited, and therefore to prevent unexpected downtimes and safety risks. CM focuses on a three-stage development process, consisting of capturing records, data processing and information management [1,2]. However, the most serious drawback in implementing this technique lies in the recording equipment (precision, resolution, sampling frequency and the number of channels) and the cost involved [3]. These considerations mainly stem from the component to be analyzed and the measurement techniques used. One of the most widely used techniques in CM is “vibration analysis”, since vibration is regarded as one of the early onset unwanted phenomena [4,5]. Conversely, bearings are the most used components in rotating machinery and with the highest probability of failure, with only 10% reaching the end of their lifespan [6,7]. According to Nguyen et al. [8], bearings have caused up to 50% of the failures in rotating machinery. Nevertheless, these components require a high sampling frequency, since the presence of incipient failures generate waves that modulate at a high frequency. According to Bernal et al. [9], the preferred frequency for the diagnosis of bearings is 50 kHz, this limits the application of CM, since the equipment with these characteristics is quite costly.

Sensors 2020, 20, 3493; doi:10.3390/s20123493 www.mdpi.com/journal/sensors Sensors 2020, 20, 3493 2 of 19

It should be note that each CM phase plays a significant role. In order to maintain consistency between these phases, the ISO 13373 standard [10,11], provides a detailed guide of the parameters to consider in each phase of CM development. It also explains that some of the considerations may be employed in other equipment not included in the standards. Meaning that the environment characteristics must be personalized, such as speed, load, temperature, power and other characteristics that may affect vibration measurement [10]. In general terms, the collection systems used are selected among those available on the market. The cost of this equipment approved for registering vibrations is drastically high, over 4000€ for one channel collecting equipment, such as the ADASH 4900 VIBRIO or RH71, increasing its value for a larger number of channels. There is other lower cost Gani and Salami equipment [12]. Conversely, commercial registering equipment such as USB-231 or Labjack U6 to name a few examples, are considered low-cost (around 270€ and 300€ respectively). They are capable of acquiring data from more than one channel at a time, but with a unit maximum sampling frequency limit of 50 kHz, which means using one single channel for analyzing certain components such as bearings. Nevertheless, a lower sampling frequency can be used to record more components such as can be seen in [13]; however this involves sacrificing masked high frequency information. In addition, there are several studies of prototypes of data acquisition systems [14–17], but most lack the data for reproducibility, the collection capacity does not meet the vibration requirements or they contain a small number of channels. Bosso et al. [14], present a multifunctional system prototype applied to railways, based on vibration and temperature collection; however, they do not test the efficiency of their algorithm relating to bearing vibration. Moreno et al. [15] display a multi-sensor data acquisition system, capable of registering high frequencies. Nevertheless, they require a computer connection to control the capturing of records, a feature that limits implementation in equipment which is difficult to access that operates in movement. Vidal and Pindado [16] present a 5-channel Arduino-based data acquisition system, however the sampling frequency is estimated at 500 Hz. In order to deal with this situation, this work proposes a low-cost data acquisition system model based on Raspberry-Pi with a high sampling frequency when registering through each one of its three channels. This model effectively meets the necessary conditions for the analysis of bearing vibrations, which are components used in over 90% of rotating machinery that have a high fault probability [6]. In addition, a practical case is presented using test bench modelling and the record captures of recordings of bearing vibrations simulating faults in rolling elements with four severity levels, in order to demonstrate the performance of the proposed system. Ultimately, the information of the vibration signals requires analysis by means of processing, for which an interface has been developed for processing the recordings in Matlab®, using the envelope analysis technique. To minimize the impact on cost, it is planned to program this analysis in an open source environment, such as C++ or Python.

2. Materials and Methods

2.1. Data Acquisition System Design The design of the data acquisition equipment has been carried out according to the bearing vibrations analysis requirements, making it essential for data to be acquired through several channels with a sampling frequency of up to 50 kHz for each one and compact enough to be easily placed in components that are diﬃcult to access. In order to meet these requirements a low-cost small-sized Raspberry PI 3 B+ computer board has been selected. This unit has an independent operating system, supports several programming languages, with remote communication capacity, operational control and data storage. The operation of the Raspberry Pi is not capable of acquiring signals from analogue sensors, since it does not include the analogue to digital conversion hardware, therefore requiring a prior stage using an external converter. Sensors 2020, 20, 3493 3 of 19

2.1.1. Raspberry Pi Speciﬁcations The Raspberry Pi (RPi) is a computer with features that meet the requirements outlined. In has an integrated Broadcom BCM2837B0 chip containing a Cortex-A53 64-bit processor, that operates at 1.4 GHz clock speed, 1 GB memory, a 2.4 GHz and 5 GHz dual band wireless connection and Ethernet Gigabit over USB 2.0 that supports up to 300 Mbps and a power supply of 3.3 and 5 V. It has a series of general purpose input/output (GPIO) pins, that represent the communication interface between the RPi and the environment. The main feature of the GPIO is that it can designate a pin as a data input or output in the software, as required. For this purpose, it incorporates communication interfaces for digital sensors type I2C, SPI and UART. In the development of this project, registration tests were carried out using I2C and SPI converters. Due to the data transfer speed being one of the main requirements, this project uses an SPI communication protocol between the Raspberry Pi and the A/D converter, since it has two data transmission lines [18]. The SPI communication interface devices have a synchronous protocol that operated in full duplex mode, to receive and transmit information through a single cable, dividing the cable into several channels to do so. Communication between both is carried out by means of a bus with 4 logic signals (MOSI, MISO, SCK, SS). The MOSI and MISO ports carry the signals in both directions, the SCK pin is the clock signal that synchronizes the data reception, and ﬁnally, the SS pin activates the corresponding slave.

2.1.2. Analogue/Digital Converter (ADC) The data acquisition and processing devices operate in a digital environment by means of an 8 bit binary value. For this purpose, the analogue variables provided by the sensors must be converted into binary equivalents. One of the main characteristics to bear in mind when selecting an ADC is the relationship between the sampling frequency and the resolution, given that it provides the capacity to obtain the positioning of the frequency and its corresponding amplitude with greater precision and sensitivity [11]. Furthermore, the communication protocol is added to these requirements, given that the ADC must allow an SPI communication in order to transfer data to the RPi. Using these considerations, it is proposed to implement an ADC based on successive approximation register (SAR), since it is ideal in data acquisition processes, where accuracy is relevant. There are some ADC models based on this architecture with similar characteristics, such as ADS8326, AD7694ARMZ and LTC1864ACS8#PBF. After tests developed, the best performance was observed in the ADS8326, additionally its low power consumption allowed the development of a more compact and portable device. Its main features include a resolution of 16 bits, obtaining a dynamic range of 90 dB, a voltage supplement (VDD) of between 2.7 and 5.5 V, a sampling frequency (Fsample) of 250 kHz, a synchronous serial interface compatible with SPI, excellent linearity and very low noise and distortion. It also incorporates a differential input and a reference voltage (Vref) may be configured from 0.1 V to VDD. This Vref is undertaken by NPC1541, which provides a low power voltage and high precision, set at 4.096 V; this is used by SAR logic to compare and determine the binary correspondence of the analog input value. The ADC performs the function of quantifying and coding the amplitude measurements transmitted from the sensor. This quantification requires that the samples taken be retained long enough to evaluate their level. Therefore, a sampling and retention circuit is required. Said function is undertaken by the MAX4518CPD analogue multiplexer, made up of four compatible channels with CMOS logic and a fast switching speed (transition time less than 250 ns), which enables several nodes to communicate at the same time with a shared transmission medium. The Figure1 shows the connection circuit diagram used in this project. Sensors 2020, 20, x FOR PEER REVIEW 4 of 19

Sensors 2020, 20, 3493 4 of 19 Sensors 2020, 20, x FOR PEER REVIEW 4 of 19

Figure 1. Analog to digital converter (ADC) electrical wiring diagram.

2.1.3. Sensors The design of the data acquisition system must be versatile regarding the number of channels recorded. As a result, the hardwareFigureFigure 1. 1. AnalogAnalog and to to d digitaligital software c converteronverter ( (ADC)AD mustC) electrical electrical be wiring wiring capable diagram diagram. . of adapting to the specified configuration as 2.1.3. required. Sensors According to ISO 13373-1 [10], the transducer used depends upon the 2.1.3. Sensors component to be analyzed,The design recommending of the data acquisition systemaccelerometers must be versatile to regarding quantify the numberthe vibration of channels rate in bearings, The design of the data acquisition system must be versatile regarding the number of channels recorded. As a result, the hardware and software must be capable of adapting to the specified as they may displayrecorded. masked As a result, faults the hardwarein high and frequency. software must be capable of adapting to the specified configuration as required. According to ISO 13373-1 [10], the transducer used depends upon the configuration as required. According to ISO 13373-1 [10], the transducer used depends upon the This acquisitioncomponent system to be analyzed, uses a recommending low-cost 805M1 accelerometers accelerometer to quantify the vibration model rate and in bearings, a threaded as connection component to be analyzed, recommending accelerometers to quantify the vibration rate in bearings, they may display masked faults in high frequency. assembly. It has asa theydynami may displayc range masked of faults ±20 in g, high a sensitivity frequency. of 100 mV/g and a flat frequency response of This acquisition system uses a low-cost 805M1 accelerometer model and a threaded connection This acquisition system uses a low-cost 805M1 accelerometer model and a threaded connection 0.4 Hz to 10 kHz (±assembly.3 dB). It This has a dynamictransducer range of incorporates20 g, a sensitivity a of stable 100 mV/ gpiezoceramic and a flat frequency crystal, response ofwith an excitation assembly. It has a dynamic range of ±20± g, a sensitivity of 100 mV/g and a flat frequency response of 0.4 Hz to 10 kHz ( 3 dB). This transducer incorporates a stable piezoceramic crystal, with an excitation voltage of 3 to 5.50.4 HzV toand 10 kHz an (± ±operating3 dB). This transducer temperature incorporates betweena stable piezoceramic −40 to crystal, +100 with °C. an Theexcitation Figure 2 shows the voltage of 3 to 5.5 V and an operating temperature between 40 to +100 C. The Figure2 shows the voltage of 3 to 5.5 V and an operating temperature between −4− 0 to +100 °◦C. The Figure 2 shows the accelerometer usedaccelerometer in this usedproject. in this project. accelerometer used in this project.

Figure 2. 805M1-0020 accelerometers.

2.1.4. Programming the Raspberry Software

The hardware designed requires a software for control. It must be intuitive and user friendly to configure, in terms of theFigure numberFigure 2.of 805M1channels 2. 805M1-0020- 0020and sampling accelerometers. accelerometers frequency to. be used. The code was developed2.1.4. Programming in C++, using the Raspberrya BCM 2835 Software library on the Broadcom chip for GPIO pins access and control, the flow chart is shown in Figure 3. 2.1.4. Programming Thethe hardware Raspberry designed Software requires asoftware for control. It must be intuitive and user friendly to conﬁgure, in terms of the number of channels and sampling frequency to be used. The code was The hardwaredeveloped designed in C++ ,requires using a BCM a 2835 software library on thefor Broadcom control. chip It for must GPIO pinsbe intuitive access and control, and user friendly to the ﬂow chart is shown in Figure3. configure, in terms of the number of channels and sampling frequency to be used. The code was developed in C++, using a BCM 2835 library on the Broadcom chip for GPIO pins access and control, the flow chart is shown in Figure 3. Sensors 2020, 20, 3493 5 of 19 Sensors 2020, 20, x FOR PEER REVIEW 5 of 19

FigureFigure 3.3. The data acquisition system operationaloperational ﬂowflow chart.chart.

OnceOnce thethe registerregister interfaceinterface has been initiated, a “.txt” filefile isis createdcreated andand savedsaved inin thethe RPi’sRPi’s SDSD memory.memory. In In addition, a multithread system is established, where the data corresponding to the the requestedrequested samplingsampling frequencyfrequency isis storedstored inin aa queue,queue, and an independent writing thread system, where thethe data of the the storage storage queue queue is is dumped dumped to to be be written written in in the the “.txt “.txt”” file. file. Afterwards Afterwards,, two two flow flow lines lines are areestablished. established. One One of these of these recognizes recognizes the theSPI SPI communication communication protocol protocol defined defined in inthe the executable executable,, it it configures the number of sensors and sets the converter clock’s maximum working frequency (Fclock). configures the number of sensors and sets the converter clock’s maximum working frequency (퐹푐푙표푐푘). Said frequency is estimated according to ADC specifications, Fclock = 24 FSample. Subsequently, a data Said frequency is estimated according to ADC specifications, 퐹푐푙표푐푘 =· 24 ∙ 퐹Sample. Subsequently, a channel,data channel, reading reading and conversion and conversion selection selection loop isloop initiated. is initiated. TheThe remaining remaining lineline requests requests the the recordingrecording configuration, configuration, requesting requesting the the sampling sampling or or writing writing frequencyfrequency i inn this this case, case, enabling enabling the the time time interval interval to be to estimated be estimated in which in which the data the are data written. are written. Once Oncethe writing the writing order order has hasbeen been initiated initiated an an identification identification loop loop ofof the the prevalent prevalent reading reading channel and correspondencecorrespondence comparisoncomparison ofof thethe datadata isis initiatedinitiated withwith thethe establishedestablished writingwriting timetime interval.interval. InIn thethe eventevent itit doesdoes notnot match, match, the the data data is is discarded discarded until until the the condition condition is met,is met, this this is doneis done independently independently for eachfor each channel. channel. Conversely, Conversely, if it does if it match, does match, the value theis value stored is in stored the multithread, in the multithread for later, dumping for later intodumping the write into thread the writ writtene thread in the written “.txt” in file; the and “.txt” the file process; and is the repeated process until is repeated the detain until writing the detain order iswriting executed, order closing is executed, the file closing and concluding the file and the concluding program. the program. InIn orderorder toto writewrite thethe data of each channel a time vector and an amplitude vector has been created. created. TheThe process starts starts when when the the desired desired sample sample rate rate is set, is set, and and the thedata data capture capture is started. is started. The time The vector time vectoris provided is provided by software, by software, using using a high a high-resolution-resolution timer timer (time.h (time.h library). library). Using Using the function function “clock_gettime”“clock_gettime” aa real-timereal-time clockclock isis accessedaccessed throughthrough thethe CLOCK_MONOTONICCLOCK_MONOTONIC function,function, withwith 8-bit8-bit precision.precision. The time vector starts at zero and isis alwaysalways increasingincreasing basedbased onon realreal time.time. As the timer rate isis higherhigher thanthan thethe samplingsampling rate,rate, whenwhen thethe timetime providedprovided byby thethe functionfunction aboveabove is aa multiplemultiple ofof thethe required sampling frequency, the time vector is accessed and the value saved for each channel. Since the N channels are connected through a multiplexer up to 1 × 4, the time of each channel varies slightly. Sensors 2020, 20, 3493 6 of 19

SensorsSensors 20202020,, 2020,, xx FORFOR PEERPEER REVIEWREVIEW 66 ofof 1919 required sampling frequency, the time vector is accessed and the value saved for each channel. Since the AsAs defineddefined inin thethe softwaresoftware structure,structure, datadata writingwriting isis notnot achievedachieved atat aa constantconstant frequency,frequency, itit N channels are connected through a multiplexer up to 1 4, the time of each channel varies slightly. thereforetherefore requiresrequires datadata processingprocessing byby meansmeans ofof subsequentsubsequent× resampling.resampling. InIn addition,addition, thethe recordingsrecordings As defined in the software structure, data writing is not achieved at a constant frequency, it dodo notnot specifyspecify thethe characteristicscharacteristics ofof thethe accelerometersaccelerometers (sensitivity(sensitivity andand offset),offset), thethe datadata obtainedobtained beingbeing therefore requires data processing by means of subsequent resampling. In addition, the recordings do inin volts.volts. Therefore,Therefore, ththeyey mustmust bebe regardedregarded inin thethe subsequentsubsequent processingprocessing ofof recordings,recordings, inin orderorder toto not specify the characteristics of the accelerometers (sensitivity and offset), the data obtained being in obtainobtain anan appropriateappropriate definitiondefinition ofof thethe magnitudemagnitude ofof thethe signal.signal. TheseThese observationsobservations willwill bebe includedincluded volts. Therefore, they must be regarded in the subsequent processing of recordings, in order to obtain whenwhen creatingcreating aa futurefuture code.code. an appropriate definition of the magnitude of the signal. These observations will be included when creating a future code. 2.1.5.2.1.5. ConnectingConnecting thethe DataData AcquisitionAcquisition SystemSystem 2.1.5.OneOne Connecting ofof thethe datadata the acquisitionacquisition Data Acquisition system’ssystem’s System prioritiespriorities isis managingmanaging toto achieveachieve aa compactcompact size,size, enablingenabling an easy integration of the system into hard-to-reach sites. Requiring all the components to be an easyOne integration of the data acquisition of the system system’s into priorities hard-to-reach is managing sites. Requiring to achieve aall compact the components size, enabling to bean integrated into a PCB board. The board must allow an easy and quick connection to the RPi, in order integratedeasy integration into a ofPCB the board. system The into board hard-to-reach must allow sites. an easy Requiring and quick all the connection components to the to beRPi, integrated in order to enable this a GPIO 40 pin adaptor has been used, only connecting the pins configured with the SPI tointo enable a PCB this board. a GPIO The 40 board pin adaptor must allow has been an easy used, and only quick connecting connection the topins the configured RPi, in order with to the enable SPI protocol. This PCB board has four USB version 2.0 ports to link the accelerometers, as is shown in the protocolthis a GPIO. This 40 PCB pin adaptorboard has has four been USB used, version only 2.0 connecting ports to link the pinsthe accelerometers, configured with as the is shown SPI protocol. in the Figure 4. FThisigure PCB 4. board has four USB version 2.0 ports to link the accelerometers, as is shown in the Figure4.

FigureFigure 4 4.4.. DesignDesign of of the the conditioning conditioning and and data data conversion conversion board. board.

InInIn addition,addition, thethe the recordingrecording recording capturecapture capture managementmanagement management isis carriedcarried is carried outout outbyby remoteremote by remote controlcontrol control connection.connection. connection. ThisThis propertyThisproperty property eliminates eliminates eliminates communication communication communication by by cableby cable cable with with with the the the recording recording recording equipment, equipment, equipment, enabling enabling remote remote recordingrecordingrecording captu capturecapturere management management and and providing providing greater greatergreater accessibility accessibility to to mechanisms mechanisms that that operate operate at at a aa distance.distance. TheseThese featuresfeatures enableenable usageusage onon a a larger larger number number of of mechanical mechanical units. units. Figure Figure 555 shows showsshows thethethe ccomponentscomponentsomponents of of the the data data acquisition acquisition system. system.

FigureFigure 5. 5. ComponentsComponents of of the the data data recording recording system system.system..

2.2.2.2. Bearing Bearing Test Test Bench Bench TheThe test test bench bench is is prepared prepared for for validating validating the the recording recording equipment, equipment, by by means means of of capturing capturing the the recordingsrecordingsrecordings ofof faultsfaults arisingarising inin the the bearings. bearings. In In addition, addition, the the equipment equipment must must be be able able to to function function under under differentdidifferentﬀerent operatoperatingoperatinging conditionsconditions in in terms terms of of regime regime and and load, load, since since these these are are considered considered to to be be the the main main parametersparameters influencinginfluencing thethe vibrationvibration raterate [19][19].. TheThe layoutlayout ofof thethe proposedproposed testtest benchbench isis shownshown inin thethe FFigureigure 66.. Sensors 2020, 20, 3493 7 of 19 parameters inﬂuencing the vibration rate [19]. The layout of the proposed test bench is shown in the

SensorsFigure 206.20,, 20,, xx FORFOR PEERPEER REVIEWREVIEW 7 of 19

Figure 6. Bearing test bench bench..

2.2.1. Traction Traction U Unitnit Traction is generated by a series BL 110110 synchronoussynchronous electric servomotor, enabling changes in revolutions with a high dynamic response. The The control control is is carried carried out out by by a a CD1 CD1-a-a amplifier, ampliﬁer, with a PMW module to regulate torque and speed, and a a “ “resolver”resolver” transmitter, transmitter, for feedback and stabilizing the parameters.

2.2.2. Bearing Bearing under under Study When selecting the test bearing, the following have been considered considered:: the the shaft shaft dimensions dimensions (20 (20 mm), mm), accessibility to to the bearing and ease ofof assembly assembly and and disassembly. disassembly. Furthermore, the the results of this project seek to be extrapolatedextrapolated in future research, to railway grease boxes, hence the need for using bearings with geometric and constructive characteristics similar to the real ones. Manufact Manufacturersurers such as SKF, FAG oror NSKNSK toto name just a few, oofferﬀer aa widewide rangerange of bearings for grease boxes, with double double-row-row roller elements and and configurations conﬁgurations from from cylindrical cylindrical to to tapered tapered rollers, rollers, depending depending on on their their application. application. Therefore, bearing in mind the restri restrictionsctions and requirements requirements mentioned mentioned previously, previously, this project used an SN-505SN-505 split casing, that will act asas thethe greasegrease box.box. The casing will contain a 22205E1KC3 doubledouble-row-row spherical roller bearing and TSNG TSNG-505-505 blocks enabling the sealing of the casing and the shaft. The The bearing bearing is is fastened fastened to to the shaft using a H H-305-305 sleeve, as shown in the FigureFigure7 7..

Figure 7. Dismantling of the test set set..

Bearing Vibration Mode A bearing is a mechanism that, in operation has its own vibrating signal, caused by the contact between the the components components it itis ismade made up up of and of and interpreted interpreted through through its fundamental its fundamental frequencies. frequencies. This signalThis signal is defined is deﬁned by the by kinematics the kinematics of the of rolling the rolling elements elements and the and Hertzian the Hertzian or elastic or elastic contact contact of these of withthese the with rolling the rolling tracks. tracks. Establishing Establishing a relationship a relationship between between the geometry the geometry of the of components the components and andthe energythe energy developed developed when when the therolling rolling elements elements pass pass over over the theload load area area [20] [.20 ]. Therefore, the presence of irregularities generate changes in the contac contactt forces of the bearing components, resulting in the generation of short duration pulses that excite the natural frequencies of the entire structure, between the bearing and the response accelerometer, modulated in high frequency ranges [7,20–22]. Figure 8 indicates the geometric characteristics used in the calculation of the frequencies and Table 1 details the frequencies at the studied speeds. The equations to calculate the frequency of the outer ring (BPFO), inner ring (BPFI), rolling elements (BSF), and the cage (FTF) are shown below. Sensors 2020, 20, 3493 8 of 19

frequency ranges [7,20–22]. Figure8 indicates the geometric characteristics used in the calculation of the frequencies and Table1 details the frequencies at the studied speeds. The equations to calculate the frequencySensors 2020 of, 20 the, x FOR outer PEER ring REVIEW (BPFO), inner ring (BPFI), rolling elements (BSF), and the cage (FTF)8 areof 19 Sensors 2020, 20, x FOR PEER REVIEW 8 of 19 shown below. 푁 퐵퐷 푁 퐵퐷 퐵푃퐹푂 = 푅푃푀 ∙ ∙ (1 − cos 훽) 퐵푃퐹퐼 = 푅푃푀 ∙ ∙ (1 + cos 훽) 푁2 퐵푃퐷BD 푁2 BD 퐵푃퐷 퐵푃퐹푂BPFO= 푅푃푀= RPM∙ ∙ N(1 −1 퐷coscos훽)β BPFI퐵푃퐹퐼 ==RPM푅푃푀N∙ 1∙+(1 +cos퐷cosβ 훽) 2 · 2 · −푃PD 2 · 2 ·2 PD 푃 (1) 푃퐷P 퐵B퐷퐷 2 h1 B 퐵퐷 i (1) 퐵푆퐹BSF= 푅푃푀= RPM∙ D∙ [11− ( D cos 훽β2) ] FTF퐹푇퐹==RPM푅푃푀1 ∙ 1 ∙ [1 −D cos( βcos 훽)] (1) 푃퐷퐵 BD 퐵퐷푃PD 2 12 PD 퐵푃퐷 퐵푆퐹 = 푅푃푀 ∙ ·퐷∙ [1· −−( 퐷cos 훽) ] 퐹푇퐹 = 푅푃푀· ∙ · ∙ [−1 − ( 퐷cos 훽)] 퐵퐷 푃퐷 2 푃퐷

FigureFigure 8. 8.Calculation Calculation of of the the fundamental fundamental frequencies frequencies of of the the bearing. bearing. In In accordance accordance with with the the geometric geometric Figurecharacteristicscharacteristics 8. Calculation of of the the bearingof bearing the fundamental used, used, a a table table frequencies of of the the frequencies frequen of thecies bearing of of each each. In component, component, accordance for forwith di differentff theerent geometric rotating rotating characteristicsregimesregimes is is shown shown of the below. below. bearing These These used, frequencies frequencies a table of havethe have frequen been been taken takencies of from from each the thecomponent, o ffiofficialcial Schae Schaeffler forffl differenter webpage. webpage. rotating regimes is shown below. These frequencies have been taken from the official Schaeffler webpage. TableTable 1. 1.Fundamental Fundamental frequencies frequencies of of the the 22205E1KC3 22205E1KC3 bearing bearing at at di differentfferent regimes. regimes. Table 1. Fundamental frequencies of the 22205E1KC3 bearing at different regimes. RegimeRegime (rpm (rpm)) 22205E1KC322205E1KC3Frequency Frequency 200Regime 350 (rpm )500 22205E1KC3 Frequency 200 350 500 BPFO 6.1852 × f 20020. 62 35036. 08 50051. 54 BPFO 6.1852 f 20.62 36.08 51.54 × BPFIBPFOBPFI 8.8148 6.81852.8148 f × ×f f 20 29.3829.62.38 3651.08. 51.4242 5173.54.46 73.46 × BSFBPFIBSF5.4030 8.58148.403 0f × ×f f 29 18.0118.38.01 5131.42. 31.5252 7345.46.03 45.03 × FTFBSFFTF 0.4123 5.0403.41230 f × ×f f 18 1.371.01.37 312.52.41 2.41 453.03.44 3.44 FTF 0.4123× × f 1.37 2.41 3.44 2.2.3. Load 2.2.3. Load 2.2.3. Load According to ISO standard 13373 [10], load is one of the most influential factors in the vibration According to ISO standard 13373 [10], load is one of the most influential factors in the vibration rateAccording of rotating to machinery, ISO standard since 13 the373 vibration [10], load rates is one vary of thesignificantly most influential under normalfactors loadin the and vibration no load rate of rotating machinery, since the vibration rates vary significantly under normal load and no load rateoperations. of rotating Therefore, machinery, a threaded since the tighteningvibration rates tower vary has significantly been used, achievingunder normal a load load of and 1.4 nokN load for a operations. Therefore, a threaded tightening tower has been used, achieving a load of 1.4 kN for a operations.tightening Therefore,torque of 7.4 a threadedNm. Figure tightening 9 shows towerthe tightening has been tower used, used achieving in this a study load .of 1.4 kN for a tightening torque of 7.4 Nm. Figure9 shows the tightening tower used in this study. tightening torque of 7.4 Nm. Figure 9 shows the tightening tower used in this study.

(a) (b) (a) (b) Figure 9. Compression load cell: (a) electrical diagram and (b) positioning in the test bench. FigureFigure 9 9.. CompressionCompression l loadoad cell cell:: ( (aa)) e electricallectrical diagram diagram and and ( (bb)) p positioningositioning in in the the test test bench bench.. An FX1901 compression load cell was used for the calculation, analyzing the ratio between the voltage,AnAn FX1901 FX1901 weight compression compressionand the tightening load load cell cell torque. was was used usedDue for forto thethe linearitycalculation, observed analyzing analyzing in the the the two ratio ratio ratios between between up to the the200 voltage,lbf (the weightcapacity andand of thethe tighteningcompressiontightening torque.torque. load Due cell),Due to theto the the tightening linearity linearityobserved torque observed required in in the the two fotworratios a ratiosweight up upto of 200to 150 200 lbf kg lbf(thewas (the capacity calculated capacity of thebyof theextrapolation. compression compression load This load cell), compression cell), the the tightening tightening load cell torque torqueuses required an required INA125P for afo weightamplifierr a weight of to 150 of condition kg150 was kg wasthe calculatedoutput voltage by extrapolation. (0–5 V) to the This load compression capacity (0– 20load0 lbf). cell On uses this an load INA125P transmission amplifier three to condition SKF 6304 - the2R output spherical voltage bearings (0–5 wereV) to usedthe load on thecapacity shaft, (0 the–20 operational0 lbf). On this frequencies load transmission of which threewere SKFtaken 6304 from- 2Rthe spherical official SKFbearings webpage were and used are on shown the shaft, in the the T ableoperational 2. frequencies of which were taken from the official SKF webpage and are shown in the Table 2. Table 2. Characteristic frequencies of the 6304-2R bearing at different regimes. Table 2. Characteristic frequencies of the 6304-2R bearing at different regimes. Sensors 2020, 20, 3493 9 of 19 calculated by extrapolation. This compression load cell uses an INA125P ampliﬁer to condition the output voltage (0–5 V) to the load capacity (0–200 lbf). On this load transmission three SKF 6304-2R spherical bearings were used on the shaft, the operational frequencies of which were taken from the oﬃcial SKF webpage and are shown in the Table2.

Sensors 2020, 20, x FOR PEER REVIEW 9 of 19 Table 2. Characteristic frequencies of the 6304-2R bearing at diﬀerent regimes.

RegimeRegime (rpm) (rpm) 6304-2R 6304-2RFrequency Frequency 200 350 500 200 350 500 BPFO 2.5783 × f 8.59 15.04 21.49 BPFO 2.5783 f 8.59 15.04 21.49 BPFI 4.4398× × f 14.80 25.90 37.00 BPFI 4.4398 f 14.80 25.90 37.00 BSF 3.5241× × f 11.75 20.56 29.37 BSF 3.5241 f 11.75 20.56 29.37 FTF 0.3687× × f 1.23 2.15 3.07 FTF 0.3687 f 1.23 2.15 3.07 × 2.3. Sensing 2.3. Sensing This section is systematized according to ISO standard 10816-7 [19]. Considering the “test bench” is structuredThis section horizontally is systematized and the bearings according are to the ISO analysis standard components 10816-7 [19 of]. Consideringinterest, the orientation the “test bench” of the isaccelerometer structured horizontally will be perpendicular and the bearings to the areshaft the and analysis located components on the bearing of interest, casings, the as close orientation as possible of the to accelerometerits center. This will study beperpendicular uses one accelerometer to the shaft for and each located bearing, on theand bearing the recordings casings, are as closeperformed as possible in the tothree its center. accelerometers This study at the uses same one time. accelerometer However, although for each bearing,the data recordings and the recordings are executed are together, performed this instudy the three focusses accelerometers on the analysis at of the a bearing, same time. connected However, to the although accelerometer the data “Ac_1”. recordings The monitoring are executed of the together,three bearings this study is carried focusses out in on order the analysisto ensure ofthat a bearing,the discrete connected frequencies to the observed accelerometer correspond “Ac_1”. to the Thebearing monitoring under study of the and three for bearingsacquiring is more carried inform outation, in order related to ensure to two thatdifferent the discretebearings, frequencies data that in observedfuture work correspond will enable to the generation bearing under of the study diagnostic and for algorithms. acquiring more information, related to two diﬀerentOn bearings,the other datahand, that it is in important future work to willobtain enable the greatest the generation sensitivity of the to diagnosticchanges in algorithms. the vibratory behavior.On the Therefore, other hand, the it is areas important of the to maximum obtain the dynamic greatest sensitivityforces of the to changes bearings in being the vibratory used are behavior.identified, Therefore, establishing the areas an orientation of the maximum of the accelerometer dynamic forces of of th thee bearing bearings casings being used at 6 areo’clock identiﬁed, (lower establishingpart), and at an 12 orientation o’clock (upper of the part) accelerometer for the load of bearing, the bearing as suggested casings at by 6 o’clockSmith and (lower Randall part), [23] and. The at 12orientation o’clock (upper of the part) accelerometers for the load bearing,is shown as in suggested Figure 10. by Smith and Randall [23]. The orientation of the accelerometers is shown in Figure 10.

FigureFigure 10. 10.Positioning Positioning and and orientation orientation of of accelerometers. accelerometers.

InIn order order to to ensure ensure maximum maximum rigidity rigidity between between the the accelerometer accelerometer and and its its respective respective positions positions an an adhesiveadhesive assembly assembly hashas beenbeen used, to to ensure ensure the the transducer transducer signal signal does does not not undergo undergo major major distortions distortions due dueto the to the resonance resonance frequency frequency of the of the type type of ofassembly assembly [24] [24. It]. should It should be be emphasized emphasized that, that, due due to to the theconstructive constructive characteristics characteristics of the of transducer, the transducer, where wherethe point the of point contact of is contact threaded is threaded(Figure 2), (Figure a magnetic2), acoupling magnetic has coupling been used has to beenensure used the perpendicularity to ensure the perpendicularity of the accelerometer of the with accelerometer the contact surface. with the contact surface. 2.4. Design of Experiment (DoE) 2.4. Design of Experiment (DoE) The DoE has been devised to validate the high frequency sampling capacity of the recording The DoE has been devised to validate the high frequency sampling capacity of the recording equipment, as well as its precision and sensitivity. Using a test based on the induction of faults in equipment, as well as its precision and sensitivity. Using a test based on the induction of faults in three rolling elements (RE) of a bearing, designed as “Rod_1”; since according to [23] in the study, this component is highlighted as being the most difficult to diagnose. It should be emphasized that, as the bearing is made up of two rows of REs, the three REs are positioned on one of these, as shown in the Figure 11. Sensors 2020, 20, 3493 10 of 19 three rolling elements (RE) of a bearing, designed as “Rod_1”; since according to [23] in the study, this component is highlighted as being the most diﬃcult to diagnose. It should be emphasized that, as the bearing is made up of two rows of REs, the three REs are positioned on one of these, as shown in the

SensorsFigure 20 1120., 20, x FOR PEER REVIEW 10 of 19

Sensors 2020, 20, x FOR PEER REVIEW 10 of 19

Figure 11. DiagramDiagram of of the the position position of the REs with induced faults faults..

This test test is is based basedFigure on on a a “Factorial11. “Factorial Diagram 5 of 5× 3”the3” design, position design, to of analyze to the analyze REs thewith theeffect induced eff ectof thefaults of thefive. fiveRE fault RE fault levels levels (F0, × F1,(F0, F2, F1, F3, F2, F4) F3, and F4) the and three the threeoperating operating regimes regimes (200, 350, (200, 500 350, rpm), 500 on rpm), the onbearing the bearing vibration vibration mode. Eachmode. Thisprocess Each test process wasis based run was threeon runa “Factorial times, three therefore times, 5 × 3” therefore design,giving a givingto total analyze of a total45 the recordings. of effect 45 recordings. of theThe five repetitions TheRE fault repetitions are levels carried (F0, are outF1,carried F2, after F3, out theF4) after faultand the the of fault three each of each rotatingoperating rotating regime regimes regime has (200, has been 350, been evaluated, 500 evaluated, rpm), inon in orderthe order bearing to to ensure ensure vibration that that mode. minor differencesEachdifferences process due due was to to uncontrollablerun uncontrollable three times, variables therefore variables are giving distributed are a distributedtotal homogenouslyof 45 recordings. homogenously throughout The repetitions throughout all the are recordings. allcarried the recordings.Theout factorsafter the andThe fault levelsfactors of considered and each levels rotating inconsidered the regime test are in has shownthe been test in are evaluated, Table shown3. in in Table order 3. to ensure that minor differences due to uncontrollable variables are distributed homogenously throughout all the recordings. The factorsTableTable and 3.3. Factorslevels considered and levels considered in the test in are the shown design of in experiment.Table 3.

LevelsLevels StudyTable Factor 3Study. Factors Factor and levels considered in the design of Unitsexperiment. Units F1F1 F2F2 F3 F3 F4 F4F5 F5 Area 0 11.05 11Levels.57 11.7 13 mm2 REAreaStudy Factor 0 11.05 11.57 11.7Units 13 mm2 RE Depth F10 0.F2006 0.F3014 0.F4019 0.F5027 mm Depth 0 0.006 0.014 0.019 0.027 mm Area 0 11.05 11Levels.57 11.7 13 mm2 ControlRE Factor Levels Units Control Factor Depth 0 R10.006 0R2.014 0.019R3 0 .027 mm Units Regime R1200 Levels350 R2 500 R3 rpm Control Factor Units R1 R2 R3 Regime 200 350 500 rpm The depth of the fault Regime generated in 200 the RE 350 is expressed500 as anrpm absolute magnitude of the difference between the normal diameter and the induced fault. The development of the fault in the REs isThe generated depth depth of ofby the themilling fault fault generated the generated surface in ofthe in each RE the is component RE expressed is expressed, asand an quantifiedabsolute as an absolute magnitude using a magnitudeDML of the IP54, diﬀ witherence of the a 1µmdifferencebetween minimum the between normal unit theof diameter measurement normal anddiameter the, as inducedshown and the in fault.induced Figure The 12 fault.. development The development of the fault of the in fault the REsin the is REsgenerated is generated by milling by milling the surface the surface of each of component, each component and quantiﬁed, and quantified using using a DML a D IP54,ML IP54, with with a 1µm a 1µmminimum minimum unit ofunit measurement, of measurement as shown, as shown in Figure in Figure 12. 12.

Figure 12. Quantification of the degradation generated in rolling elements.

One of the mainFigure requirements 12. QuantificationQuantiﬁcation in analyzing of the degradation vibrations generated in bearings in roll rolling is ingthe elements.elementshigh sampling. frequency [9], a feature that validates the recording equipment proposed. Therefore, these recordings were taken at theOne equipment’s of the main maximum requirements sampling in analyzing frequency, vibrations of 45 kHz in to bearings be used is in the 3 channels; high sampling the equipment’s frequency recording[9], a feature capacity that validates is detailed the inrecording Section equipment3.1. In the subsequent proposed. Therefore, data processing, these recordings the same 40were kHz taken are resampledat the equipment’s in order maximumto ensure a sampling constant frequency,sampling frequency of 45 kHz t tohroughout be used in the 3 channels;entire recording. the equipment’s recordingTable capacity 4 provides is detailed details in of S ection the conditions 3.1. In the ofsubsequent the medium data processing, (uncontrolled the variables)same 40 kHz in theare developmentresampled in order of the to experiment,ensure a constant since sampling these are frequency very important throughout variables the entire and recording. may influence the resultsTable of the 4 experimentprovides details [25]. of the conditions of the medium (uncontrolled variables) in the development of the experiment, since these are very important variables and may influence the results of the experiment [25]. Sensors 2020, 20, 3493 11 of 19

One of the main requirements in analyzing vibrations in bearings is the high sampling frequency [9], a feature that validates the recording equipment proposed. Therefore, these recordings were taken at the equipment’s maximum sampling frequency, of 45 kHz to be used in 3 channels; the equipment’s recording capacity is detailed in Section 3.1. In the subsequent data processing, the same 40 kHz are resampled in order to ensure a constant sampling frequency throughout the entire recording. Table4 provides details of the conditions of the medium (uncontrolled variables) in the development of the experiment, since these are very important variables and may inﬂuence the results of the experiment [25].

Table 4. Uncontrolled variables in the design of experiment.

Uncontrolled Factors Value Units

Ambient Temperature 20 to 24 ◦C Operating temperature of casing 35 to 45 ◦C Relative humidity 40 to 52 % Atmospheric Pressure 1010 to 1016 hPa

Considering the DoE is made up of three replications for each treatment, the one that showed the most representative value in the experiment was evaluated. The identiﬁcation of the experimental unit or interest replication, will enable its treatment and representation in the analysis of the fault evolution. For this an analysis between replications, the correlation (Cor), covariance (Cov) and coherence (Coh) of each treatment was determined within the frequency domain by means of FFT. A list of the relationship developed for each treatment is shown below, comparing their replications.    Cor1,2 Cor1,3 Cor2,3     Cov Cov Cov   1,2 1,3 2,3  (2)   Coh1,2 Coh1,3 Coh2,3

Subsequently, in order to determine the most representative replication of each treatment, the “geometric mean” (µR,i) was determined between each ratio estimator contained in the replication under analysis and between the three estimators analyzed, selecting the one with the highest value, which indicates a lower data dispersion and ensuring the absence of atypical values in the recordings.  q  3 p p p  µR1 = Cor1,2 Cor1,3 Cov1,2 Cov1,3 Coh1,2 Coh1,3  q · · · · ·  3 p p p  (3)  µR2 = Cor2,1 Cor2,3 Cov2,1 Cov2,3 Coh2,1 Coh2,3  q · · · · ·  3 p p p  µR = Cor Cor Cov Cov Coh Coh 3 3,1· 3,2· 3,1· 3,2· 3,1· 3,2 2.5. Data Processing As in the recording phase, “data processing” is another critical phase for the success of the diagnostic process using condition monitoring [26]. This factor is considered in [11] as a set of activities used for participating in the signals acquired, developing the filtering of noise or other undesired signals, formatting signals of interest and applying various available representation methods, in order to facilitate the identification of the characteristics of the mechanism under study by the analyst. Gupta and Pradhan [20], suggest that the data processing technique must be considered in accordance with the analysis component. Therefore, considering the bearing’s oscillating fundamentals, this study uses the “envelope analysis”, given that it mainly seeks to detect resonance areas excited or modulated in amplitude by periodic impact forces, whose repetition frequency is an indicator of where the defect is located and its amplitude, a measurement characterizing the status of the component [27]. This process involves the use of a sequence of operations with the vibration signal, that initiates by eliminating the low frequency components associated to other conditions of the rotating equipment, Sensors 2020, 20, 3493 12 of 19 such as imbalance and misalignment [28]. The signal’s envelope is then determined, obtained in this study by using the Hilbert transform. The removal of interference components is executed by filtering the signal, making it necessary to determine the band of frequencies to analyze, in order to obtain the greatest amount of information on the status of the bearings. McInerny and Dai [29], describe that most of the diagnostic equipment enable the analysis of signals in bands of 1–2.5, 2.5–5, 5–10, 10–20 and 20–40 kHz. As an example, the diagnostic and data collector unit “Adash 4900 Vibrio Ex”, uses 3 bands (0.5–1.5, 1.5–5 and 5–16 kHz) for detecting faults at high frequency. In this work, the selection of these bands is described in Section 3.2.

3. Results and Discussion In order to investigate the performance of the recording equipment proposed for analyzing mechanical vibrations, this section presents the results obtained by means of recorded data processing. This database is available, the section “Supplementary Materials” is the access link. Similarly, a brief description of the recording equipment is presented and the fundamentals considered in the selection of the band-pass ﬁlter. Subsequently, the results deﬁned in Table3 of the fault diagnostics in rolling elements are discussed.

3.1. Validation of the Recording Equipment This section details the tests performed to validate the recording equipment regarding its performance and sampling capacity through each channel. The equipment’s performance evaluation consisted of the capture of known frequency waves (gradually increasing their frequency) generated by an oscilloscope, observing a high precision in their identification. In order to learn about the equipment’s recording capacity per channel, a quantitative analysis is performed to determine the maximum sampling frequency when using a certain number of channels. This confirms the design of the equipment proposed, for its high frequency data acquisition capacity. This study is based upon data acquisition at a frequency of 110 kHz, for different numbers of channels, and a length of 3 min. The results show that, as the number of channels used increases, the maximum sampling frequency is reduced. Table5 with the analyzed results is shown below, detailing the maximum average sampling capacity per number of channels employed and the standard deviation by which the sampling frequency fluctuates.

Table 5. Details of the recording capacity of the equipment per channel.

No. of Channels Sampling Frequency [kHz] Standard Deviation [Hz] 1 110 16,280 2 65 7085 3 45 4403 4 35 3057

As can be observed, the recording equipment proposed can achieve a sampling frequency of over 100 kHz using one channel. This represents an advantage when compared to other recording equipment, such as the USB-231 or Labjack U6; the maximum sampling frequencies of which for one channel are less than half of those achieved in this study. Another representative advantage is related to its cost; which, depending upon the supplier of the main components, can be obtained from 70€ (without considering the sensors); an estimated value of three times less, when compared respectively with the equipment mentioned beforehand.

3.2. Determining the Analysis Band The estimate of the analysis band is based on the study of discrete frequency components proposed in [23], where by using the “power spectral density” (PSD) technique, it evaluated the band frequencies in which the discrete components tend to increase their amplitude. This analysis was performed on Sensors 2020, 20, 3493 13 of 19 the experimental units of each treatment, observing similarities in the bands proposed, even when increasing the rotating regime. As an example, the following shows a representation of recording 15, associated to the first fault level in rolling elements. This figure, suggests frequency bands of (0.2–1.8 103; 1.8–6.1 103; 6.1–9.2 103 and 9.2–12.8 103 Hz) for the filtering prior to the treatment × × × × of signals, where it has been observed that the discrete components tend to increase energy. The following figure represents the envelope analysis of the bands suggested in Figure 13, where the higher areas of signal energy were observed. In cases a and b, the increase in amplitude of the frequencies close to characteristic BSF frequencies and their respective harmonics is evident; unlike cases c and d, where the frequency amplitudes of interest are distorted by the background noise.

AlthoughSensors 2020,the 20, x ﬁrst FOR twoPEER casesREVIEW are the most relevant, case a shows a large presence of FTF, rotation13 of 19 frequency, respective harmonics, and frequencies not related to the fault characteristic, that show less inﬂuenceand frequencies in case not b. related Conversely, to the thefault number characteristic, of harmonics that sho relatedw less influence to the fault in case is more b. Conversely, noticeable the in casenumber b. of harmonics related to the fault is more noticeable in case b.

FigureFigure 13.13. Power spectral density ofof the record 1515 (40(40 kHz,kHz, rollingrolling elementelement failure,failure, failfail levellevel 1,1, regimeregime thethe 500500 rpm).rpm). ProposedProposed bandsbands forfor envelopeenvelope analysis.analysis.

AccordingAccording toto CarterCarter [[30]30],, thethe amplitudeamplitude modulationmodulation generatedgenerated byby defectsdefects inin thethe bearingbearing willwill bebe quicklyquickly attenuated,attenuated, asas thethe analysisanalysis frequencyfrequency increases.increases. This can be seen inin FigureFigure 1414,, where,where, asas thethe analysisanalysis bandband increases increases the the amplitudes amplitudes of of the the frequencies frequencies of interestof interest decrease. decrease. However, However, observing observing the backgroundthe background noise noise and and the easythe easy identiﬁcation identification of the of characteristicthe characterist faultic fault frequency, frequency, this this study study uses uses the secondthe second band band suggested, suggested, between between 1.8 to 6.1 1.8 to10 3 6.Hz;1 × where 103 Hz; more where precision more was precision observed was in observed identifying in × theidentifying frequency the and frequency harmonics and of harmonics interest. of interest.

3.3. Detection of Faults in “Rolling Elements” The following shows the degradation of the rolling elements in this study for a regime of 200 rpm, in which it is difficult to identify the fundamental frequency BSF or its harmonics. Nevertheless, an increase in trend of the frequencies close to BSF is observed and its corresponding harmonics, as the severity of the fault grows, as shown in Figure 15. Smith and Randall [23], define the classical envelope spectrum vibration mode for rolling elements as a representation of two indications, the first of these manifested as harmonics of BSF with side bands modulated at FTF frequency, and the second corresponding to low amplitude FTF harmonics. However, very few bearings with these faults present these symptoms, making them difficult to diagnose. Conversely, the following figure shows a clear presence of the second indication in the spectrum obtained, with representative amplitudes; which according to Graney and Starry [6], are an indicator that the bearing is reaching the end of its service life. This characteristic(a) shows that the magnitude of damage caused(b in) the rolling elements of this study is drastic. In this case a change in the vibration spectrum can be seen between the normal recording and the recordings with induced faults, maintaining a similar behavior between the induced fault levels.

Figure 14. Envelope analysis of recording 15, using bands: (a) a band of 0.2–1.8 × 103 Hz, (b) a band of 1.8–6.1 × 103 Hz, (c) a band of 6.1–9.2 × 103 Hz, (d) a band of 9.2–12.8 × 103 Hz. Sensors 2020, 20, x FOR PEER REVIEW 13 of 19

and frequencies not related to the fault characteristic, that show less influence in case b. Conversely, the number of harmonics related to the fault is more noticeable in case b.

Figure 13. Power spectral density of the record 15 (40 kHz, rolling element failure, fail level 1, regime the 500 rpm). Proposed bands for envelope analysis.

According to Carter [30], the amplitude modulation generated by defects in the bearing will be quickly attenuated, as the analysis frequency increases. This can be seen in Figure 14, where, as the analysis band increases the amplitudes of the frequencies of interest decrease. However, observing Sensors 2020, 20, 3493the background noise and the easy identification of the characteristic fault frequency, this study uses 14 of 19 the second band suggested, between 1.8 to 6.1 × 103 Hz; where more precision was observed in identifying the frequency and harmonics of interest.

Sensors 2020, 20, x FOR PEER REVIEW 14 of 19

3.3. Detection of Faults in “Rolling(a) Elements” (b) The following shows the degradation of the rolling elements in this study for a regime of 200 rpm, in which it is difficult to identify the fundamental frequency BSF or its harmonics. Nevertheless, an increase in trend of the frequencies close to BSF is observed and its corresponding harmonics, as the severity of the fault grows, as shown in Figure 15. Smith and Randall [23], define the classical envelope spectrum vibration mode for rolling elements as a representation of two indications, the first of these manifested as harmonics of BSF with side bands modulated at FTF frequency, and the second corresponding to low amplitude FTF harmonics. However, very few bearings with these faults present these symptoms, making them difficult to diagnose. Conversely, the following figure shows a clear presence of the second indication in the spectrum obtained, with representative amplitudes; which according to Graney and Starry [6], are an indicator that the bearing is reaching the end of its service life. This( ccharacteristic) shows that the magnitude of damage(d) caused in the rolling elements of this study is drastic. In this case a change in the vibration spectrum can be seen between Figure 14. Envelope analysis of recording 15, using bands: (a) a band of 0.2–1.8 × 103 Hz, (b) a band Figurethe 14. normalEnvelope recording analysis and the of recordings recording with 15, usinginduced bands: faults, ( maintaininga) a band of a 0.2–1.8similar behavior103 Hz, between (b) a band of of 1.8–6.1 × 103 Hz, (c) a band of 6.1–9.2 × 103 Hz, (d) a band of 9.2–12.8 × 103 Hz. × 1.8–6.1the induced103 Hz, fault (c) alevels. band of 6.1–9.2 103 Hz, (d) a band of 9.2–12.8 103 Hz. × × ×

FigureFigure 15. Representation 15. Representation of of recordings recordings of of rolling rolling element element degradation degradation at 200 rpm at. 200 rpm. Sensors 2020, 20, x FOR PEER REVIEW 15 of 19 Sensors 2020, 20, 3493 15 of 19 SensorsThe 20 three20, 20, firstx FOR FTF PEER harmonics REVIEW can be observed in the results. This number of harmonics coincides15 of 19 with the number of damaged rolling elements in this test, being more noticeable in the envelope TheThe three three first ﬁrst FTF FTF harmonics harmonics can can be be observed observed in in the the results. results. This This nu numbermber of of harmonics harmonics coincides coincides analysis performed in the 9.2–12.8 × 103 Hz band, where there is a greater representation of 3 × FTF withwith the the number number of of damaged damaged rolling rolling elements elements in in this this test, test, being being more more noticeable noticeable in in the the envelope envelope for this case, as can be seen in Figure 16. As was mentioned beforehand, this evidence is an indicator analysisanalysis performed performed in thethe 9.2–12.89.2–12.8 × 101033 HzHz band,band, wherewhere there there is is a a greater greater representation representation of 3of 3FTF × FTF for that the bearing is getting close to collapsing.× × forthis this case, case, as canas can be seenbe seen in Figurein Figure 16. 16. As As was was mentioned mentioned beforehand, beforehand, this this evidence evidence is an is indicatoran indicator that thatthe bearingthe bearing is getting is getting close close to collapsing. to collapsing.

Figure 16. Representation of recordings of rolling element degradation at 200 rpm, analysis band of 9.8–12.8 × 103 Hz. FigureFigure 16. 16. RepresentationRepresentation of of recordings recordings of of rolling rolling element element degradation degradation at at 200 200 rpm, rpm, analysis analysis band band of of 3 99.8–12.8.8–12.8 × 10103 HzHz.. Figure 17 ×shows the representation of the degradation of rolling elements at 350 rpm; in which a behaviorFigure similar 17 shows to the the one representation above can be ofobserved. the degradation This spectrum of rolling also elements shows an at impulsive 350 rpm; in content which a Figure 17 shows the representation of the degradation of rolling elements at 350 rpm; in which inbehavior the side bands similar of to the the BSF one harmonics, above canbe which observed. grows This as the spectrum fault severity also shows increases. an impulsive This criterion content is in a behavior similar to the one above can be observed. This spectrum also shows an impulsive content takenthe sideinto bands consideration of the BSF by harmonics, Smith and which Randall grows [23] as, attributing the fault severity two possible increases. causes This as criterion the answer, is taken in the side bands of the BSF harmonics, which grows as the fault severity increases. This criterion is theinto first consideration as a result of by the Smith amplitude and Randall modulation [23], attributing in response two to possible random causes impulses, as the that answer, are not the related ﬁrst as a taken into consideration by Smith and Randall [23], attributing two possible causes as the answer, toresult the specified of the amplitude fault; the modulation second is in due response to the to presence random impulses, of extraneous that are loose not relateddebris towhich the speciﬁed cause the first as a result of the amplitude modulation in response to random impulses, that are not related occasional pulses. tofault; the the specified second fault; is due the to the second presence is due of extraneous to the presence loose of debris extraneous which cause loose occasional debris which pulses. cause occasional pulses.

FigureFigure 17. 17. RepresentationRepresentation of ofthe the Envelope Envelope of recordings of recordings of ofthe the rolling rolling element element degradation degradation at 350 at 350 rpm, rpm, 3 3 in ina band a band of of1.8 1.8 × 10310 to 6to.1 6.1× 103 10Hz.Hz. Figure 17. Representation× of ×the Envelope of recordings of the rolling element degradation at 350 rpm, in a band of 1.8 × 103 to 6.1 × 103 Hz. Sensors 2020, 20, 3493 16 of 19 Sensors 2020, 20, x FOR PEER REVIEW 16 of 19 Sensors 2020, 20, x FOR PEER REVIEW 16 of 19 Since the bearing was dismantled, cleaned and lubricated in each fault induction, ruling out the SinceSince thethe bearingbearing waswas dismantled,dismantled, cleanedcleaned andand lubricatedlubricated inin eacheach faultfault induction,induction, rulingruling outout thethe possibility of impurities in the lubricant and the behavior of the bands is attributed to, the amplitude possibilitypossibility ofof impuritiesimpurities inin thethe lubricantlubricant andand thethe behaviorbehavior ofof thethe bandsbands isis attributedattributed to,to, thethe amplitudeamplitude modulation of successive impulses of a random nature. This impulsive behavior generated by faults modulationmodulation of of successive successive impulses impulses of of a a random random nature. nature. This This impulsive impulsive behavior behavior generated generated by by faults fau inlts in RE has also been observed by Amini et al. [31], in his analysis of a severely damaged railway grease REin RE has has also also been been observed observed by by Amini Amini et et al. al. [31 [31]], in, in his his analysis analysis of of a a severely severely damaged damaged railway railwaygrease grease box bearing. boxbox bearing.bearing. Figure 18 represents the degradation spectrum of rolling elements at 500 rpm, the behavior of FigureFigure 1818 representsrepresents thethe degradationdegradation spectrumspectrum ofof rollingrolling elementselements atat 500500 rpm,rpm, thethe behaviorbehavior ofof which is similar to the previous ones and where the influence of the rotating speed is noticeable in whichwhich isis similar similar to to the the previous previous ones ones and and where where the the inﬂuence influenc ofe theof the rotating rotating speed speed is noticeable is noticeable in the in the 1 × BSF amplitude, which is more than five times and almost double of the degradation at 200 1the BSF1 × BSF amplitude, amplitude, which which is more is more than than ﬁve timesfive times and almostand almost double double of the of degradation the degradation at 200 at and 200 and× 350 rpm respectively, when comparing the more severe fault cases in each operating regime. 350and rpm 350 respectively,rpm respectively, when when comparing comparing the more the more severe severe fault casesfault cases in each in operatingeach operating regime. regime.

Figure 18. RepresentationRepresentation of recordings of ro rollinglling element degradation at 500 rpm. Figure 18. Representation of recordings of rolling element degradation at 500 rpm. Testing whether the number of FTF harmonics is related to the number of damaged REs has also Testing whether the number of FTF harmonics is related to the number of damaged REs has also been performed. This was done by repeating the analysis with 5 damaged REs, only for an F4 grade been performed. This was done by repeating the analysis with 5 damaged REs, only for an F4 grade fault and under three rotating regimes (200,(200, 350350 and 500 rpm). Figure 19 presents this analysis for 200 fault and under three rotating regimes (200, 350 and 500 rpm). Figure 19 presents this analysis for 200 and 500 rpm, where the presence of certain FTF harmonics are observed; and depending upon the and 500 rpm, where the presence of certain FTF harmonics are observed; and depending upon the analysis band, the FTF harmonics are more representative.representative. In this case, in the presence of 5 REs with analysis band, the FTF harmonics are more representative. In this case, in the presence of 5 REs with faults the presence of the ﬁrstfirst 5 harmonics is not evident, ruling out the hypothesis assumed for the faults the presence of the first 5 harmonics is not evident, ruling out the hypothesis assumed for the case of the 3 damaged REs, where a large presencepresence ofof 33 FTFFTF harmonicsharmonics waswas observed.observed. case of the 3 damaged REs, where a large presence of 3 FTF harmonics was observed.

(a) (b) (a) (b) Figure 19. Envelope Analysis for a bearing with 5 damaged REs,REs, ﬁlterfilter bandband at:at: 1.8–6.11.8–6.1 × 103 HzHz (blue) (blue) Figure 19. Envelope Analysis for a bearing with 5 damaged REs, filter band at: 1.8–6.1× × 103 Hz (blue) band, 6.1–9.8 × 101033 HzHz (red) (red) and and 9.8–12.8 9.8–12.8 × 10103 3HzHz (green); (green); for: for: (a ()a )200 200 rpm, rpm, and and (b (b) )500 500 rpm. rpm. band, 6.1–9.8× × 103 Hz (red) and 9.8–12.8 × 103 Hz (green); for: (a) 200 rpm, and (b) 500 rpm. Sensors 2020, 20, 3493 17 of 19

4. Conclusions A data acquisition unit has been developed, with a 16-bit resolution and high frequency data capture, reaching 110, 65, 45 and 35 kHz using 1, 2, 3 and 4 channels respectively. Although it is geared to the analysis of bearing vibrations, it is a multi-purpose unit that can measure any signal measured in volts, in the 0–5 V range. Its efficiency has been tested by means of a DoE, prepared for the analysis of faults in bearings. The data acquisition system suggested in this analysis has been tested to effectively meet the conditions required for vibration analysis, confirming that the development of a low-cost diagnostics unit with a high sampling frequency in each channel base on this prototype is feasible. Furthermore, as this unit is RPi based, data storage is carried out directly in its memory bypassing any connection to an external computer for data transmission, while enabling remote communication for remote record capture control. In addition, its design enables implementation in difficult access areas, due to its compact size. However, the unit designed requires a signal resampling stage, due to a variation in the sampling frequency, a characteristic required for data analysis in the frequency domain. This drawback will be addressed in future developments, in order to ensure the stability of the recording frequency, and therefore reduce the total working time. The data represented have been processed by means of envelope analysis, enabling the satisfactory identification of the faults generated in the rolling elements. Through which, the RE defects observed do not present a classical envelope vibration mode, but rather manifest themselves through impulsive content bands in their frequency (BSF) and harmonics. Another significant characteristic to point out, is the close relationship of the BSF with FTF, observing that as the fault severity grows the FTF frequency amplitude and harmonics increase. In addition, the influence of the vibration rate rotation has been proven, drastically increasing the frequency amplitude associated to the fault, as the speed increase. These results, consistent with other researchers [23,31], validate the efficiency of the recording unit designed. The clear advantage of this unit in comparison to other commercial units, considered to be low-cost (described in Section 3.1), is the two-fold increase in recording capacity, plus its reduced cost at less than half the price. There are other low-cost commercial units with higher capabilities (myDAQ from National Instruments), with a maximum sampling frequency of 200 kHz; however, their price is five times more in comparison with the unit suggested. As future processing work, other types of analyses will be implemented, such as spectral kurtosis to better identify the analysis band and the analysis of independent components in order to identify the signals of the bearing under analysis, in order to implement an intelligent fault detection system based on artificial intelligence (neural networks, support vector machines) using these outputs.

Supplementary Materials: Database available at: https://zenodo.org/record/3898942#.Xuo94kUzZPY. Author Contributions: C.R.S.-O. wrote the paper and performed the measurements, processing and analysis of data; C.R.S.-O. and J.D.C.-M. developed the state-of-the-art and methodology; J.M.M. and J.D.C.-M. coordinated the project administration, resources and provided some suggestions in the writing and revision of the text; J.M.M., J.D.C.-M., C.R.S.-O. and J.L.G.-B. designed the data acquisition system and vibration test bench. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Acknowledgments: The first author would like to thank CITEF for its financial support and CITEF’s personnel, in special to Ramón Gil González and Victor Prieto for their assistance and help in the realization of this project. Conflicts of Interest: The authors declare no conflict of interest.

References

1. Heng, A.; Zhang, S.; Tan, A.C.C.; Mathew, J. Rotating machinery prognostics: State of the art, challenges and opportunities. Mech. Syst. Signal Process. 2009, 23, 724–739. [CrossRef] Sensors 2020, 20, 3493 18 of 19

2. Qiu, H.; Lee, J.; Lin, J.; Yu, G. Robust performance degradation assessment methods for enhanced rolling element bearing prognostics. Adv. Eng. Inform. 2003, 17, 127–140. [CrossRef] 3. Austerlitz, H. Data Acquisition Techniques Using PCs; Academic Press: San Diego, CA, USA, 2002. 4. Liu, S.; Wang, S. Machine Health Monitoring and Prognostication Via Vibration Information. In Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications, Jinan, China, 16–18 October 2006; Volume 1, pp. 879–886. [CrossRef] 5. Mahalungkar, S.; Ingram, M. Online and manual (oﬄine) vibration monitoring of equipment for reliability centered maintenance. In Proceedings of the IEEE-IAS/PCA 2004 Cement Industry Technical Conference (IEEE Cat. No04CH37518), Chattanooga, TN, USA, 25–30 April 2004; pp. 245–261. [CrossRef] 6. Graney, B.P.; Starry, K. Rolling Element Bearing Analysis. Mater. Eval. 2012, 70, 78. 7. Yu, K.; Lin, T.R.; Tan, J.; Ma, H. An adaptive sensitive frequency band selection method for empirical wavelet transform and its application in bearing fault diagnosis. Measurement 2019, 134, 375–384. [CrossRef] 8. Nguyen, P.; Kang, M.; Kim, J.-M.; Ahn, B.-H.; Ha, J.-M.; Choi, B.-K. Robust condition monitoring of rolling element bearings using de-noising and envelope analysis with signal decomposition techniques. Expert Syst. Appl. 2015, 42, 9024–9032. [CrossRef] 9. Bernal, E.; Spiryagin, M.; Cole, C. Onboard Condition Monitoring Sensors, Systems and Techniques for Freight Railway Vehicles: A Review. IEEE Sens. J. 2019, 19, 4–24. [CrossRef] 10. IS/ISO 13373-1. Condition Monitoring and Diagnostics of Machines—Vibration Condition Monitoring, Part 1: General Procedures; Bureau of Indian Standards: New Delhi, India, 2002; p. 57. Available online: https: //archive.org/details/gov.in.is.iso.13373.1.2002/page/n49/mode/2up (accessed on 10 August 2019). 11. IS/ISO 13373-2. Condition Monitoring and Diagnostics of Machines—Vibration Condition Monitoring, Part 2: Processing Analysis and Presentation of Vibration Data; Bureau of Indian Standards: New Delhi, India, 2005; p. 38. Available online: https://archive.org/details/gov.in.is.iso.13373.2.2005 (accessed on 10 August 2019). 12. Gani, A.; Salami, M.J.E. A LabVIEW based data acquisition system for vibration monitoring and analysis. In Proceedings of the Student Conference on Research and Development, Shah Alam, Malaysia, 17 July 2002; pp. 62–65. [CrossRef] 13. Had, A.; Sabri, K. A two-stage blind deconvolution strategy for bearing fault vibration signals. Mech. Syst. Signal Process. 2019, 134, 106307. [CrossRef] 14. Bosso, N.; Gugliotta, A.; Zampieri, N. Design and testing of an innovative monitoring system for railway vehicles. Proc. Inst. Mech. Eng. Part F J. Rail Rapid Transit 2018, 232, 445–460. [CrossRef] 15. Moreno, C.; González, A.; Olazagoitia, J.L.; Vinolas, J. The Acquisition Rate and Soundness of a Low-Cost Data Acquisition System (LC-DAQ) for High Frequency Applications. Sensors 2020, 20, 524. [CrossRef] [PubMed] 16. Vidal-Pardo, A.; Pindado, S. Design and Development of a 5-Channel Arduino-Based Data Acquisition System (ABDAS) for Experimental Aerodynamics Research. Sensors 2018, 18, 2382. [CrossRef][PubMed] 17. Guo, H.; Yu, H.; Sun, C.; Zhang, Z.; Zheng, E. Continuous and Real-Time Vibration Data Acquisition and Analysis System Based on S3C6410 and Linux. In Proceedings of the 2013 Fifth International Conference on Measuring Technology and Mechatronics Automation, Hong Kong, China, 16–17 January 2013; pp. 389–392. [CrossRef] 18. González, A.; Olazagoitia, J.; Vinolas, J. A Low-Cost Data Acquisition System for Automobile Dynamics Applications. Sensors 2018, 18, 366. [CrossRef][PubMed] 19. ISO 10816-7. Mechanical Vibration—Evaluation of Machine Vibration by Measurements on Non-Rotating Parts—Part 7: Rotodynamic Pumps for Industrial Applications, Including Measurements on Rotating Shafts. 2009. Available online: https://www.iso.org/obp/ui/#iso:std:iso:10816:-7:ed-1:v1:en (accessed on 17 November 2019). 20. Ghafari, S.H. A Fault Diagnosis System for Rotary Machinery Supported by Rolling Element Bearings. Ph.D. Thesis, University of Waterloo, Waterloo, ON, Canada, 2007. 21. Gupta, P.; Pradhan, M.K. Fault detection analysis in rolling element bearing: A review. Mater. Today Proc. 2017, 4, 2085–2094. [CrossRef] 22. Randall, R.B.; Antoni, J. Rolling element bearing diagnostics—A tutorial. Mech. Syst. Signal Process. 2011, 25, 485–520. [CrossRef] 23. Smith, W.A.; Randall, R.B. Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study. Mech. Syst. Signal Process. 2015, 64–65, 100–131. [CrossRef] Sensors 2020, 20, 3493 19 of 19

24. IS 14883. Mechanical Vibration and Shock—Mechanical Mounting of Accelerometers; Bureau of Indian Standards: New Delhi, India, 2000; p. 18. Available online: https://archive.org/details/gov.in.is.14883.2000 (accessed on 10 August 2019). 25. ISO 10816-3. Mechanical Vibration—Evaluation of Machine Vibration by Measurements on Non-Rotating Parts—Part 3: Industrial Machines with Nominal Power Above 15 kW and Nominal Speeds between 120 r/min and 15 000 r/min When Measured In Situ. ISO: 2009. Available online: http://www.iso.org/cms/render/live/en/sites/isoorg/ contents/data/standard/05/05/50528.html (accessed on 17 November 2019). 26. Yang, H.; Mathew, J.; Ma, L. Fault diagnosis of rolling element bearings using basis pursuit. Mech. Syst. Signal Process. 2005, 19, 341–356. [CrossRef] 27. Samanta, B.; Al-Balushi, K.R. Artiﬁcial neural network based fault diagnostics of rolling element bearings using time-domain features. Mech. Syst. Signal Process. 2003, 17, 317–328. [CrossRef] 28. Feng, G.-J.; Gu, J.; Zhen, D.; Aliwan, M.; Gu, F.-S.; Ball, A.D. Implementation of envelope analysis on a wireless condition monitoring system for bearing fault diagnosis. Int. J. Autom. Comput. 2015, 12, 14–24. [CrossRef] 29. McInerny, S.A.; Dai, Y. Basic vibration signal processing for bearing fault detection. IEEE Trans. Educ. 2003, 46, 149–156. [CrossRef] 30. Carter, D.L. Rolling element bearing condition testing method and apparatus. U.S. Patent 5,477,730, 26 December 1995. 31. Amini, A.; Entezami, M.; Papaelias, M. Onboard detection of railway axle bearing defects using envelope analysis of high frequency acoustic emission signals. Case Stud. Nondestruct. Test. Eval. 2016, 6, 8–16. [CrossRef]

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).