Ijnsupervised М\Eural Computation for Event Identification

$Ijnsupervised М\Eural Computation for Event Identification$

IJnsupervised Ì\eural Computation for Event Identification in Structural Health Monitoring Systems

Loren Card

,,. i.äî:1ä:#:ïiJï0", In Partial F\lfillment of the Requirements for the Degree of

Master Of Science

Department of Electrical and Computer Engineering University of Manitoba Winnipeg, Manitoba

@Loren Card, July 2004 TIIT'. I.INTVERSITY OF MANITOBA

FACIILTY OF GRADUATE STUDIES ***** COPYRIGHT PERMISSION

Unsupervised Neural Computation for

Event Identification in Structural Health

Monitoring Systems

Loren Card

A ThesisÆracticum submitted to the Faculty of Graduate Studies of The University of

Manitoba in partial fulfiilment of the requirement of the degree of

MASTER OF SCIENCE

Loren Card @ 2004

Permission has been granted to the Library of the University of Manitoba to lend or sell copies of this thesis/practicum, to the National Library of Canada to microfilm this thesis and to lend or sell copÍes of the fÏIm, and to University Microfilms Inc. to publish an abstract of this thesÍs/practicum. This reproduction or copy of this thesis has been made available by authorify of the copyright owner solely for the purpose of private study and research, and may õoty u. reiroduced ana ðopiea as permitted by copyright laws or with express rwitten authorization fiom the copyright owner. Abstract

This thesis report explores the use ofunsupervised neural computation for event identification (EID) in structurar hearth monitoring (sHM) systems. EID techniques are useful in sHM systems for minimizing the size of sHM data sets, and the costs associated with analysing, transmitting and storing sHM data.

The approach to EID explored. is adaptive, self-configuring and does not require detailed information about the structure being monitored.

A frequency sensitive competitive learning (FSCL) technique is used to

model the output of an sHM system. sHM system output states which dis-

agree with the model are deemed "novel" and identified as SHM events. The EID system pERL is implemented in and operates on sHM data stored by a database server running the MySeL DBMS software.

The EID system is evaluated with sHM data from three structures including the Taylor Bridge, the Portage creek Bridge and the Gorden Boy statue. The

EID system is able to identify strain gauge events of 0.75¡.te, 12.5¡te, 1.25¡'e or smaller in the sHM measurement data from the Taylor Bridge, the portage creek Bridge, and the Golden Boy respectivery. The EID system is able to identify accelerometer events of .00459, 0.00209 or smaller in the sHM mea.surement data from the Portage Creek Bridge, and the Golden Boy respectively.

The EID system is compared to a simprified event identification (S-EID) system, which does not use power spectral density estimation or unsupervised neural computation. The s-EID system is shown to be effective but less sensitive than the EID system to sHM events. The EID system is capabre of adapting to noisy environments.

some example sHM events, believed to be the result of seismic activity, from the Portage Creek Bridge are presented and discussed. Acknowledgements

would I like to thank my wife and family for their love and support. I am blessed to have them, and wouid be lost without them. I would like to thank my advisor, Dr. Dean K. McNeil, for his friendship and guidance throughout my career as a graduate student. I would like to thank Dr. Douglas Thomson, for insightful comments and recommendations regarding the research in this thesis. special thanks go to Dr. Aftab Mufti and ISIS canada, for providing access to the structural health monitoring data and systems which made this work possible. I would also like to thank Liting Han for her help working with the ISIS sHM data aquisition systems. I gratefully acknowledge the financial support of the Natural sciences and Engineering Research council of canada (NSERC).

. /Loren Contents

L lntroduction 6 1.1 Problems with SHM Add¡essed bv Event Identification . 6 L.2 EID System Design Considerations I

IJnsupervised Neural Computation 10

IJnsupervised Neural computation for Event rd.entification L3

3.1 The EID System 13

3.1.1 An SHM Data Source 13

3.L.2 Windows of SHM Data 13

3.1.3 Normalization of Measurment Values 15

3.1.4 Power Spectral Density Estimation . 15

3.i.5 Unsupervised Neural Computation 15

3.1.6 EID System Output: A Novelty Index 16

3.I.7 SHM System Event HandÌer 16

3.2 System Apparatus and Implementation Notes 16

EID System Performance Characterization 19

4.I EID System Behaviour 24

4.2 EID System Sensitivity 25

4.3 Comparing the EID System to a Simplified Approach 30

4.3.L Normalization of Measurment Values 31 qt 4.3.2 Root Sum of Squared Changes in Sensor Channel Outputs dù

4.3.3 S-EID System Output: A Simplified Novelty Index òJ

4.4 S-EID Vs. EID System Performance JÐ

Seismic Events Affecting Portage Creek Bridge 42

Conclusions and Future'Work 47 List of Figures

Basic 1 A SHM System. 7

2 Basic Neural Network Architecture. 10

3 Unsupervised Neural Computation. 11 EID 4 System Using Unsupe¡vised Neural Computation . . . . . 14 5 EID System Data Flow and Data Structures lT Taylor 6 Bridge, Headingley, Manitoba, Canada . . . . . 19 Portage 7 Creek Bridge, Victoria, British Columbia, Canada . 20

8 The Golden Boy, A Statue in Winnipeg, Manitoba, Canada 20 I Tlpical Sensor Output and Novelty plot from Taylor Bridge EID

System 22

10 Tlpical Sensor Output and Novelty plot f¡om portage Creek BridgeElDSystem ...... 28 11 Tlpical Sensor Output and Novelty plot from Golden Boy EID

System 28

12 A Simple SHM Event 24

13 A More Complex SHM Event 25 14 sensitivity of raylor Bridge's EID system to sinusoidal Excita-

tion of Strain Gauge Channels 2g 15 sensitivity of Portage creek Bridge's EID system to sinusoidal

Excitationof StrainGaugeChannels ...... 2g i6 sensitivity of Portage creek Bridge's EID system to sinusoidal

Excitation of Accelerometer Channels 29 L7 sensitivity of Golden Boy's EID system to sinusoidal Excitation

of Strain Gauge Channels Zg 18 sensitivity of Golden Boy's EID system to sinusoidal Excitation

of Accelerometer Channels . 30 19 simplified EID (S-EID) system without power spectrar Density

Estimation or Neural Computation g2

20 EID System Output 84

2l S-EID System Outpur 84 22 sensitivity of raylor Bridge's S-EID system to sinusoidal Exci- tation of Strain Gauge Channels g5

23 sensitivity of Portage creek Bridge's s-EID system to sinusoidal

Excitationof StrainGaugeChannels ...... 86 24 sensitivity of Portage creek Bridge's s-EID system to sinusoidal

Excitation of Accelerometer Channels 86 25 sensitivity of Golden Boy's s-EID system to sinusoidal Excita-

tion of Strain Gauge Channels JT 26 sensitivity of Golden Boy's s-EID system to sinusoidal Excita-

tion of Accelerometer Channels BT

27 EID Model of SHM System Output 40 28 S-EID Model of SHM System Ourput 4t

29 Seismic Event 1 - Identified by EiD System (Center peak) . . . . 43

30 Seismic Event 1 - Most Active Strain Gauge 48

31 Seismic Event 2 - Identified by EiD System 44

32 Seismic Event 2 - Most Active Strain Gauge 44

33 Seismic Event 3 - Identified by EID System 45

34 Seismic Event 3 - Most Active Strain Gauge 45

List of Tables

1 Summary of SHM System Sensors 22

2 Summary of EID System Sensitivity 27

3 Summary of EID Vs. S-EID System Sensitivity 38

4 Seismic Events at Portage Creek Bridge 42 1 fntroduction

The practice of structural Hearth Monitoring[l] (sHM) is gaining popularity and interest in the engineering community. stewards of civil infrastructure are increasingly recognizing a need to monitor structures, in order to better assess structural "health." Aging infrastructure and limited maintenance budgets re_ quire innovative approaches to ensure public safety while balancing costs. sHM technology has the potential to heip increase safet¡ reduce life cycle costs, im- prove maintenance strategies, and provide important structural performance feedback to designers. This thesis explores the use of unsupervised neurar computation for event

identification (EID) in sHM systems. EID mechanisms have severar apprications

in sHM systems. They can be used to solve some key problems associated with current SHM practice.

1.1 Problems with sHM Addressed by Event rdentifica- tion

Conceptually, the practice of sHM is straightforward. A structure is fitted with a number of sensors in key locations. sensors monitor physical quantities such as strain, acceleration, displacement, or temperature. A data acquisition (DAe) system continuously interrogates the sensors and records measurements to a database. occasionally, "events" occur which excite a structure. The resulting SHM measurements reflect the struc[ure's behaviour during the event. using data analysis techniques, sHM measurements are used to make an assessment of the structure's "health." A general outline of a typical sHM system is shown in Figure 1. Analog Sensor Outputs

Digitized Measurments

Recent or Historic Measurements

SHM Data Analysis

Results of Analysis

Figure 1: A Basic SHM System. practice, In sHM systems present some chailenges. one chailenge currentry facing many sHM system projects is the rate at which sHM data is collected. single A sensor channel, sampled with 16 bits, at 100H2, will generate about 17 megabytes of data every day. A DAe system with 100 such sensor channels record will about 630 gigabytes of data in a year. consider that an owner may have thousands of structures in an inventory, and the amount of data involved can become overwhelming.

The transmission, storage, and analysis of every raq/ sensor measurement

is expensive and impractical. Fortunately, effective sHM can be accomplished without retaining a complete sensor record. sHM data is by nature repetitive and consistent. By identifying events in sHM data, the costs associated with manipulating such large data sets can be mitigated.

sHM data analysis (by a human or a machine) can be focused on key events, rather than endless monotonous sHM measurements. A researcher, or bridge owner might be interested in the number of healy trucks passing over a bridge. The reaction of the bridge to the heavy truck tra,ffic may also be of interest. An

EID system could help identify the times at which such events occurred. sHM data storage costs can be addressed by using an EID system such as the one explored in this thesis. since sHM data tends to be so monotonous, it is mo¡e efficient to discard a portion of the uneventful sensor readings. Lowering sampling rates to lessen the burden of data storage is not appropriate since the higher-frequency components of a structure's behavior are then lost. simply

discarding sHM data at regular intervars (for exampre keeping only the readings

from the first few minutes of every hour) is problematic since the readings associated with any novel event may be lost. sHM measurements associated with events can be stored in preference to monotonous readings. Keeping a small subset of the monotonous readings at regular intervals, in addition to the events, can satisfy long-term structural analysis needs. sHM data communication costs can be drastically reduced with the help of an EID system. An sHM system which blindly transmits every measurement it acquires, may be wasting vast communications resources. such an sHM system may be transmitting measu¡ements which amount to the structure saying

"Nothing is happening." An sHM system which incorporates an EID process

could remain silent most of the time, occasionally reporting ,,This event just happened."

L.2 EID System Design Considerations

There is currently a need for versatile sHM methods, which can be applied to

a wide variety of structures. civil infrastructure inventories may be diverse

and may include many styles and types of structures. In academia, researchers

have the luxury of designing an sHM system tailored specifically for a showcase structure. Most civil structures are not showcase pieces. Most do not merit custom-designed sHM systems. owners do not have the resources to tailor sHM systems to a structure beyond identifuing the number, types, and locations for sensors to be used. utilitarian everyday structures are important however, and merit generic "install it, and forget it," styred sHM systems. such generic sHM systems, deployed ubiquitously, will be the kind which prove most usefur, and reduce ownership and maintenance costs.

The approach to EID explored in this thesis is agnostic of the structure it monitors, and should be useful in many sHM systems. Every attempt has been made to keep the system as versatile, automated, and robust as possible, without sacrificing system performance. In order to help maximize the utility of the EID system, it has been designed to configrue itself, and operate without supervision. Given a source of SHM data, the system will autonomously identify those sHM measurements which represent events or structural activity. x4 Xn

Figure 2: Basic Neural Network Architecture.

2 IJnsupervised Neural Computation

Neural computing systems were originally inspired by the function of animal brains[2], where many simple interconnected learning units (neurons) operate in parallel to achieve their collective task. The strength of the interconnections between the individual neurons are adjusted so that the system can adapt and "learn." Figure 3 shows a graphical representation of a basic neural network structure. Neural computing systems are commonly applied where algorithmic solutions to a problem are difficult, but an abundance of example data is available with which to train the system. For example, recognition of hand w¡itten characters from image data is difficult to accomplish algorithmically, but a suit- able neural computing system may learn the task when trained with enough examples.

Figure 3 is a graphical representation of an unsupervised neural computing system. Neural network input vectors (represented by white circles) are mapped to a multi-dimensional input space. (2-D in this simple example.) Learning units

10 Multi.Dimensional Neural Network lnpul Space

O.Ê Novel Input

Figure 3: Unsupervised Neural Computation.

(represented by black diamonds) are arso positioned in the space. A ,,novelty detector" can be buiìt by associating a "Noverty Index', (ffr) with the input vectors. The 1t/1 of an input vector is the distance to the closest learning unit. The unsupervised computing system uses a learning algorithm to train itself to identify "novelty" by adjusting the position of the learning units. The learning units are positioned in the multidimensional input space to reflect the most common"mundane" input states. Novelty is detected when an input disagrees with the model represented by the learning units. (Graphically a novel input is one which is located far away from any ìearning unit.)

Many different types of learning algorithms exist[3]. The results reported in this thesis make use of an approach known as Ftequency sensitive competitive Learning (FSCL). FSCL has been used in the past for vecto¡ quantization[4] and in robot vision systems[5].

11 In this thesis, the output gr¿ of a given learning urit ri is simpry the Eucridean

distance between its input vector æ and weight vector to¿.

a¿: llu¿ - æll (1) Each learning unit i is said to "compete', to have its weight vector u;¿ updated. For each input vector æ presented to the network, a single winning unit is selected based upon its output !¿ and, its success in winning previous com- petitions. Each learning unit maintains a count c¿ of the number of times it has won. The winner, denoted ri*, is the rearning unit with the smalrest y¿c¿ product.

i- : i, l (g¿: mi,n(g¿c¿)) (2)

using the minimum g¿q product as the criteria for choosing the winning learning unit makes the selection process sensitive to the frequency with which the learning units win. A frequency-sensitive competition ensures that all of the learning units participate in the system. The weights of the winning unit '.u¿. are updated by adjusting them slightly towards æ according to equation B. Here e is used as a learning rate parameter, typically set between 0.0001 and 0.001.

: ?ui.neu ?Di.old. * e(la¿.o¿¿ - æ) (3)

The overall effect of this system is that the learning units, weight vectors . . .'tDn lr! will move to represent a model of all the æ input vectors the system has

seen. As stated previousl¡ a novelty detector (or event identification system) can be built by using the minimal g¿ distance between æ and, us¿ as a ,,Noveity

Index," designated .ðy'.I.

NI : mi,n(U¿) (4)

t2 3 LJnsupervised Neural Computation for Event Identification

3.1 The EID System

This section describes how an unsupervised neural computing system can be used to create an event identiflcation (EID) system for the analysis of sHM data. Figure 4 shows a conceptual overview of the EID system. The following

subsections describe the EID process in detail, and the apparatus used for the EID system implementation.

3.1.L An SHM Data Source

The first component of the the EID system is a source of sHM daia. The EID system can operate on data collected directry from a DAe system. (For example, to limit the amount of data later stored or transmitted.) Alternatively,

the EID system can operate on a stored database of sHM measurements. (For

example, to focus an analysis process, or implement a data decimation scheme.)

'Windows 3.1-.2 of SHM Data

The sHM data is separated into 2-second windows for analysis. 2-second windows were chosen since this interval seems to represent an approximate time- interval for many types of sHM events. Generaily speaking, a bridge subjected

to a heavy vehicle traffic event will react and return to its normal resting state

within a couple of seconds. windows with a 50% overlap were chosen so that no

sHM event could occur between windows undetected. Events which last longer than 2-seconds are still detected, as long as they cause significant activity in the sHM system sensor outputs within a 2-second interval. similarly, events which occur in smaller time intervals are also detected, so long as they aìso cause signifi cant activity.

i3 DAQ System or Other SHM Data Source

2-Second Windows of SHM Data From Sensor Channels

2-Second Windows of SHM Data From Sensor Channels (Scaled)

Sensor Channel Power Spectrum Levels

Unsupervised Neural Computation (FSCL)

Novelty lndex

SHM System Event Handler

Figure 4: EID System Using Unsupervised Neural Computation

L4 3.L.3 Normalization of Measurment Values

once the sHM data in separated into b0% overrapping, 2-second windows, it is

normalized to give each channel equal influence in the neural computing process.

The mean value of each channel, over the window, is calculated and subtracted from each data sample. This is done to make the EID system insensitive to

slow trends in sensor output (such as daily thermal cycles). Each sensor mea-

surement is also scaled by a normalization value, forcing sensor values to range

approximately between plus one and minus one. This scaling is mainly an effort to reconcile the different units of measure used by different sensor types. (For example, microstrain vs. g's)

3.L.4 Power Spectral Density Estimation

The third step in the EID system is a power spectral density estimation, of each channel across the data in a 2-second window. A power spectral density is used instead of a standard FFT operation since signar phase is arbitrary. This step re- duces the number of data points involved by a factor of two. The power spectral density estimation was originally an attempt to help characterize the natural frequencies of structures. In section 4 we see the EID system (in its current

.configuration) is noi especially sensitive to the natural frequencies exhibited by structures. The power spectral density estimation is useful however, to help the neural computing system characterize noise levels on sensor channels.

3.1.5 (Jnsupervised Neural Computation

The power levels of each sensor channel, at each frequency are presented to the neural computing system for assessment. The chan¡rel power spectrum levels define a point in a high dimensional space. (pigure 3 depicts a 2-D neural computation space. The Golden Boy for example, has 87 sensor channels sampled at 32H2. 2-second windows of Golden Boy sHM data result in a 11g4-

15 dimensional space.) For the experiments in this thesis, eight rearning units are used. The learning units characterize the sHM system output state, and generate a "Novelty Index" (.v1) value for each 2-second window of sHM data.

3.1.6 EID System Output: A Novelty Index

The main output of the EID system is a singre ry'1 varue associated with each window of sHM system sensor readings. (This is the same -ô/1 described in equation 4') Plotting the .n// values versus time reveals "novel sensor events,, as spikes against a background of "mundane sensor events,,. When sensor channels exhibit any activity over a 2-second window, the EID system's output will jump to several times, severar hundred times, or severar thousand times its mean output value.

3.L.7 SHM System Event Handler

Finall¡ process a or application makes use of ly'r values. This ihesis has briefly described some of the applications of an EID system such as data decimation, and optimizing us'ge of communications resources. These apprications are not explored in detail.

3.2 System Apparatus and Implementation Notes

The EID system was implemented using the pERL programming ianguage with commodity PC hardware. An EID program interacts with a database of sHM measurements hosted by a Mysel database server. The EID program reads sHM measurement values from a data tabre, performs anarysis and writes a ly'I value to a separate table. The database also hosts tables which store the learning unit weight vectors and normarization values used in the EID process. Figure 5 diagrams the interaction of the EID application with sHM data and other tables.

16 SHM Measurement Database (MySQL Table)

Figure 5: EID System Data FIow and Data Structures

The EID program is designed to be largely self-configuring. The process requires only a database table of SHM measurements. It checks for the existence of a table containing normalization values. If the normalization table does not exist, it is created by calculating a standard deviation for each sensor channel from the first two hours of sHM data. once the normalization table is created, it is reused and not modified.

The EID program also checks for the existence of a rearning unit weight vector table and creates it as required. weights are initialized randomly. The

FSCL process allows the EID program to update the weights with every window of SHM data, and the EID program stores rnodified weight vectors back in the database table periodically. when the EID process is run subsequent times, it benefits from the weights stored previously. ,À[f values are stored in a separate database table. This table is also created as required. .À{1 values are keyed by the date and time of the associated window of SHM data.

17 PERL rvas chosen for its flexibility and robust support for database interac-

tion. Despite being a high-level, interpreted computer language, pERL proved to be capable of performing sHM data manipuration tasks. The current EID system implementation is portabÌe, and has run successfully on Limrx, Macos

X, and FreeBSD. Some minor modification of path variables wouid be required to run the sofbware on MS Windows.

The execution speed of the EID system was not formally characterized. It was found however, that a 450 MHz pentium III desktop computer, running the EID software while hosting the sHM data, was abre to process Taylor Bridge

sHM data (14 sensor channels with 32 Hz sampling) at about five times real-time speed' (Each unit of sHM data took one fifth as long to process as collect.) A more current desktop computer, with an AMD AthlonXp 2500+ (1.gGhz) pro- cessor was able to process the same data set at about B0 times real-time speed. one might expect that the FFT operation is the most time-consuming operation in the EID system. ExperimenLation with the simpiified EID system (described in Section 4) indicates that the main obstacle to increased performance is in fact the large number of database calls in the current EID program implementation. The cu¡rent implementation presents opportunities for optimization.

18 Figure 6: Taylor Bridge, Headingley, Manitoba, Canada

4 EID System Performance Characterization

The EID approach presented in this thesis has been evaruated with three separate structures. These include two bridges, the Taylor Bridge in Heading- ley Manitoba, canada (Figure 6) and the Portage creek Bridge in victoria,

British columbia, canada (Figure 7). The third structure is a public statue, the Golden Boy (Figure 8), which is a 5m tall gilded bronze statue mounted at the pinnacle of the main government building in winnipeg Manitoba, canada

77m above grade. These three structures are equipped with mechanical and fiber optic strain gauges, temperature sensors, and accelerometers. Data acquisition (DAQ) equipment is used to continuously monitor the structures taking measurements 32 times per second.

19 Figure 7: Portage Creek Bridge, Victoria, British Columbia, Canada

Figure 8: The Golden Bo¡ A Statue in Winnipeg, Manitoba, Canada The Taylor Bridge is equipped with 12 strain gauges, and two temperature sensors. The strain gauges are located on the bridge's girders. six ofthe gauges are ìocated mid-span, and six are rocated near supports. The mid-span sensors are naturally more active then the support sensors. The Tayror Bridge,s 14 sensor channels, sampled at J2 Hz, taken in two-second windows results in 44g_ dimensional input vectors after being normalized and Fourier transformed. The primary source of excitation of the Taylor Bridge is vehicle traffic. since the bridge is not located on a busy route, vehicre traffic is rerativery sparse. Tayror Bridge sees a few heavy trucks on a daily basis. The Portage Creek Bridge is equipped with 30 strain gauges, two 3_axis accelerometers, and one temperature gauge. The portage creek Bridge,s 3z sensor channels, sampled at 82Hz, taken in two-second windows results in 11g4_ dimensional input vectors after being normalized and Fou¡ier transformed. The Portage creek Bridge's sensors are ail located on support colum¡rs. The bridge sees lots of commuter traffic on a regular basis. It is also located in a region subjected to seismic activity. The Golden Boy is equipped with five strain gauges, two 3-axis accererome- ters, and one temperature gauge. The Gorden Boy's 13 senso¡ channers, sampred at 32 Hz, taken in two-second windows results in 416-dimensional input vectors after being normalized and Fourier transformed. The Golden Boy,s principal source of excitation is the wind. It has a natural frequency near 3 Hz. As one might expect, the Golden Boy tends to oscilrate more on windier days.

2I Table 1: Summary of SHM System Sensors

Structure Strain Accelerometer Temperature Input

Name Gauge Channel Sensor Vector Count Count Count Dimensionality

Tayior Bridge 12 0 2 448 Portage Creek Bridge 30 6 1 1 184

Golden Boy 5 6 , 416

TaforBftge, (2003-$20 1O:O:O) + (0..4095 . 1s) -22 ¡ .z¿ I -ru I 'õ -28 sf [ *, I' ç .*[

3AF""|- 40L 0 512 1024 1536 2o4S 2560 æ72 35a4 4{196 120

100

80 !

60 o z 40 20

o 0 512 1024 1536 204A 2580 go72 35S4 4096 Seænds

Figure 9: Tlpical sensor output and Novelty plot from Taylor Bridge EID System

22 PortÊgecreek, (2003-4-23 t3:54:æ) + (0..4095 . 1s) 30, : StainT_1_c2 - - *l I StainTJ_e. 'õ ,01 i StrainS_l_c2 I 't5 I e jj 'È ro I *'*'*''-;'*-å-'i-¡;i-'-' .I ;- i-. *l tUt-'t^i'*-i'¡''-:vr*"-L"11^'t' ot **li ir-* ì"':-r--l--.-ï-'-' _.I ,a: el :l E c :f zo Íf 2l ;t 2048 3072 Sæonds

Figure 10: T)'pical sensor output and Noverty prot from portage creek Bridge EID System

GoldenBoy, (2003-9-26 iO:OO) + (0..4095 . is) 10 I 8 7 É 6 o 5 .9 4 È 3 2 1 0 -1

'10 I

7 È 6 ; 5 o 4 2 3 2 '| o 204A Seconds

Figure 11: Tþical sensor output and Novelty prot from Gorden Bov EID System Portâgecreok, (2003-12-18 23:9:1 g) + (O..S.t.t . .tßZs)

Sba¡n8_l_cl -.----

56 g

'õ 52 e 'I so

! t: 4A ¡'lf iJ , ¡, ii 46 ¡¡r I rLr¡ ¡,1l.1Jrrr¡¿¡;ir i-4íl l'iú.1 il'¡,ì¡lÍ"i iir'1,' _' f r' '¡'¡t I ti: tt:

42 I Seconds

Figure 12: A Simple SHM Event

4.7 EID System Behaviour

Figures 9, 10, and 11 show some typical EID system output plots (bottom), with a few sensor channels (top). Note that spikes in the EID system output

correspond to those in the sensor channel plots. The output of the EID system can be considered an assessment ofthe state ofthe structure at any time. struc_ tural events result in spikes in the EID system output. Note that in Figure 11, the EID system output is more erratic, reflecting the gusty nature of the wind which excites the Golden Boy. Figures 12, and 13 show examples of some sHM events the EID system is able to identify. Figure 12 is a simple event, where a strain gauge's measurements have changed by several microstrain in a few seconds. Figure L3 shows a more complex event where several strain gauge outputs have oscillated by several microstrain in a few seconds. The EID system is capable of identifying many types of such sensor events. The system is sensitive to events which cause

24 Portagscræk, (20O3-+2S 4:5SlS) + (0..S1 1 . r/32s)

.E go

.t0 6 I 12 .t4 16 Sæonds

Figure 13: A More Complex SHM Event

changes in the power spectra of the SHM sensors channels. Any change in one or more sensor channels' measurements over one window of SHM data t, seconds) will affect the output of the EID system.

4.2 EID System Sensitivity

Having briefly expiored the kinds of sHM events which can be identified by the

EID system, it is desirable to characterize precisely the sensitivity of the system.

In order to determine the sensitivity of the EID system, samples of sHM data were artificially modified by adding sinusoidal signal components to them. The reaction of the EID system to the addition of different sim¡soidal signals to its inputs is plotted in Figures 14 through 1g. Note that the EID system output has been normailized in Figures 14 through 1g so that an ,,average" or ,,mund.ane', event is scaled to unity. This scaling gives some basis for comparing the ÐID system output across various structures. Table 2 summarizes the results of the tests.

25 Figure 14 expÌores the EID system's sensitivity with rayror Bridge's sHM system. The eight curves piotted show how the EID system's output varies with the addition of different signaìs. Although the plot is crowded with eight different curves, they are rabered on the pìot in the order in which they appear. The steepest' tightest curve on the reft side of the prot is the Tayror Bridge,s EID system response to a r0 Hz sinusoidar signar added to the six strain gauge channels located near the span supports. The curve furthest to the right represents the Taylor Bridge's EID system response to a 4.5 Hz sinusoidal signal added to the six strain gauge channels located mid_span. The sinusoidal signals were added to a set of 10 baseline 2-second windows of sHM data, previously ,,mundane,,. identified by the EID system as An average of the EID system's response across these 10 modified windows is shown in the sensitivity plots.

The minimum EID system output rever required to designate an ,,event,, was chosen (somewhat arbitrarily) to be 1.5. some basis for this decision can be found be examining the EID system output levers in Figure 20. The EID system output only rarely exceeds a value of 1.5. As can be expected, the Tayror Bridge's EID system is more sensitive to the excitation of many channers at once, than to the excitation of a singre sensor channel. Also as expected, the Taylor Bridge's EID system is more sensitive to excitation of the strain change located near the supports, than those located mid-span. The system's insensitivity to the frequency of sinusoidal excitation was unexpected. The Tayior Bridge has a naturar frequency near 4.5 Hz, but the EID system is only very slightly less sensitive at this frequency.

26 Table 2: Summary of EID System Sensitivity

Structure Minimum Strain Minimum Accelerometer

Name Event Detectable Event Detectable Taylor Bridge 0.75p.e NA

Portage Creek Bridge 12.5¡,te 0.00459

Golden Boy I.25p.e 0.00209

Figure 15 shows the Portage creek Bridge's EID system response to s! nusoidal excitation of some of its (30) strain gauges. Note the variation in sensitivity from the "Docile" to the "Active" strain gauges. This variation in sensitivity is due to different normalization (scaling) values allocated to each sensor channel during the initial calibration of the EiD system. The calibration process indicated the EID system should expect more activity on the strain gauge ,,Docile',, channel denoted "Active" than the one denoted and has com- pensated accordingly.

Figure 17 shows the Golden Boy's EID system's response to stimulation of its strain gauge channels. 3 Hz and 10 Hz frequencies were chosen to test the EiD system's response to characteristic and non-characteristic frequency events. The Golden Boy has a natural frequency near 3 Hz, and does not usually exhibit an 10 Hz oscillation. It was hoped that the EID system would be able to discriminate between events exhibiting the Golden Boy's natural frequency, and those which do not. Figure 1z shows the system does discriminate, although not significantly. Figure 17 shows the Golden Boy's EID system is only slightiy less sensitive to event which exhibit 3 Hz oscillations.

27 6

5.5

o 4.5 Ê 4 q o ü

it 2.5 z 2 -! E 1.5 2 1

0.5

0 0.0 0.5 0.75 1.0 1.25 1.5 Ampl¡lud€ ofAdded Sinusoidat Compon€nt (uS)

Figure 14: sensitivity of raylor Bridge's EID system to sinusoidal Excitation of Strain Gauge Channels

5.5

o 4,5 E 4 ø ô ú 3.5

; 2.5 z 2 -N õ 1.5 E z 1

0.5

0 5 10 15 20 Ampl¡tuds ofAdded 't0Hz Slnusoidal Componont (uS)

Figure 15: sensitivity of Portage creek Bridge,s EID system to sinusoidal Ex- citation of Strain Gauge Channels

28 6 All (6) A@lêrcmêt€rs,---"-. Singlê A@leromêtor,....,.,.

o 4.5 E 4 o U ; õ 2.5 z 2 ! 1.5 E z 1

0.5 .1.5 lndêx of Smallest Events Detælable =

0 0.000 0.002 0.004 0.006 0.008 Ahplitudo ofAddod iOHz Sinusoldal Compon€nt (g)

Figure 16: sensitivity of Portage creek Bridge's EID system to sinusoidal Ex- citation of Accelerometer Channels

5.5

o Ê 4 o U

õ z 2 -! E 1.5 z 1

0.5

0 0.0 0.5 'f.0 1.5 2.0 3.0 Amp¡¡tudo ofAdded S¡nuso¡dal Component (uS)

Figure 17: sensitivity of Golden Boy's EID system to sinusoidal Excitation of Strain Gauge Channels

29 6 All (6) A@lsËmeters i 5.5 All (6) AæglbromêteE t! S¡nglê Acdrl€rometer I I Singlê âÉælerometei .! ,! J 4.5 I Ê '¡ 4 :! q ,! o E J 3 ! à 2.5 !ì z .! 1.5 z 1

0.5 lndex ofrsmal¡êst EventsiDêl€ctable = 1.5 0L 0.000 0.001 0,002 0.003 o.oo4 Amplitude ofAdded S¡nusoidal Componênt (g)

Figure 18: sensitivity of Golden Boy's EID system to sinusoidal Excitation of Accelerometer Channels

4.3 Comparing the EID System to a Simplified Approach 'we have seen that an unsupervised neural computing approach to EID can be effective. some may argue that the approach is too complicated, and that similar

functionality might be gained using a simplified approach. The most common portage output state of the Taylor Bridge, creek Bridge, and Gorden Boy sHM

systems is a mundane state which represents very little activity. This mundane

state is characterized by flat power spectra on each channel. In the time domain, such mundane windows of sHM data are flat unchanging sequences of sensor measurements. This most common, mundane output state represents the origin

in the EID system's multi-dimensional neural computation space. The power density estimation step of our EID system is computationally intensive. An EID system could be made more computationally efficient without it. Estimating the signal power on a sensor channel can be achieved in a much cruder way by subtracting the minimum sensor value from the maximum

30 sensor value in a window of sHM data. since the EID system does not seem to characterize the natural frequencies of the structures tested previously, such crude power level estimation might be appropriate.

unsupervised neural networks are capable ofcharacterizing complex patterns

in neural computation spaces. It may seem superfluous to use a neural network characterize to the output state of an sHM system which amounts to a mult! dimensional zero-crossing. such an output state couÌd be more simply modelled with a multi-dimensional zero-vector.

These ideas lead to the concept of a simplified event identification system (S-EID) which does not use an FFT for power spectrar density estimation, and does not use a neural network to characterize the common, mundane output state of an sHM system. Figure 19 depicts a s-EID system to herp exprore the costs and benefits associated with the EID system central to this thesis. Key aspects of the S-EID system are discussed below.

4.3.L Normalization of Measurment Values

Just as with the EID system, sHM data is normalized to give each channel equal influence in the s-EiD system. scaring is still useful to reconcile the different units of measrre used by different sensor types.

31 DAQ System or Other SHM Data Source

2-Second Windows of SHM Data From Sensor Channels

Sensor Channel Normalization

2-Second Windows of SHM Data From Sensor Channels (Scaled)

Root Sum of Squared Changes in Sensor Channels

Simplified Novelty lndex

SHM System Event Handler

Figure 19: simplified EID (S-EID) system without power spectral Density Estimation or Neural Computation

DO 4.3.2 Root sum of squared changes in sensor channer outputs

Instead of performing a power spectral density estimation and neural network

analysis on windows of sHM data, the s-EID system assumes that the common, mundane output state of alt sHM systems is a state of inactivity. The s-EID system assumes that any changes in the channel outputs in a window of sHM data represents an event of interest.

A range ofvalues for each sensor channer is carculated for the 2-second win-

dow of sHM measurements. Each sensor range value is squared, and summed. The root of the sum is taken, resuiting in a "simplified Novelty Index', (s¡ü1).

Each window of sHM measurements is associated with a s N I value, used as an an output from the S-EID system:

(5)

4.3.3 S-EID System Output: A Simplified Novelty fnd.ex

The main output of the s-EID system is a ,g-l{r value (analagous to the EID system's Nr) associated with each window of sHM system sensor readings. Just as with the ,,novel EiD system, plotting the ^9r{r values versus time reveals sensor events" as spikes against a background of "mundane sensor events,,. The main difference is the s-EID turns out to be less sensitive than the EID system to sHM events. Figures 20 and 21 show EID and s-EID system output from analysing the same Portage creek Bridge sHM data. Note the peaks in the EID system output plot are much more pronounced.

JJ Portagocre€k, (200,É29 1g:S4:gg) + (0..2047' ts) 5

4.5

3.5

x3 s > 2.5 z2

1.5

0.5

0 1024 't7s2 Seænd6

Figure 20: EID System Output

Portag€Cr€€k, (2009-23 13:54:39) + (0..2047 . ts) 1.6

1.4

! i o.s z 0.6

o.4

512 1024 1 538 Seænds

Figure 21: S-EID System Output

34 6

5.5

E 4.5 ø o 4 U 3.5

E 3

à õ z .!

E 1 z 0.5

0 0.0 0.s 1.0 1.s 2.0 2.5 3.0 3.5 4.O 4.s s,o AfiplitudÊ ofAdded Sinusoldat Component (uS)

Figure 22: sensitivity of raylor Bridge's s-EID system to sinusoidal Excitation of Strain Gauge Channels

4.4 S-EID Vs. EID System performance.

Figures 20 and 21 seem to indicate that the original EID system is more sensitive to sHM events than the s-EID system. The same tests as performed on the

EID system in section 4.2, where performed on the s-EID system. The results are shown in Figures 22 to 26. A summary of the test results is shown in Table 3. There is one difference in how the test results were interpreted for the s-EID system. The minimum normalized S-EID system output used to designate an sHM event was reduced from 1.5 to 1.3 since the range of output values for "mundane sHM system outputs" is slightly narrov/er for the s-EID system than the EID system. Figures 20 and 21 shows the narrower range of output values for "mu¡dane events" with the S-EID system.

35 6 Gauge '-j-.-. 5.5 ìeuoês...... ,. (Active) Gauoe ---- o E 4.5

o 4 ú

.E 3 ; 6 2 z .! E 'l z 0.5 lndÞx of SnEll€sl Ëv6nls Dotætablê F l:3 :. . -

0 0 5 10 t5 20 25 30 3s 40 45 50 55 60 Amplìtudê ofAdded 1oHz S¡nusoidat Component (uS)

Figure 23: sensitivity of Portage c¡eek Bridge,s s-EID system to sinusoidal Excitation of Strain Gauge Channels

6 AJI (6) / 5.5 Single = _è o 5 E 4.5 E Ø o 4 U 3.5

ø.E 3 s -à 2 z 1.5 .N E z 0.5 0L 0.000 0.008 0.012 0.016 o.o2o 0.024 Ampl¡tudo ofAdded 10Hz Slnuso¡dal Component (g)

Figure 24: sensitivity of Portage creek Bridge's s-EID system to sinusoidal Excitation of Accelerometer Channels

36 6 AJI SrEln câusòs (iOHi) DED -'-.-.-- 5.5 At¡ StEin cauqssl3Hzl DED ,..,..,,. Single Stra¡n Gauõe lîOHzf DED -----. Singte Sù-ain caugd(3Hzt DED - - -- 5 E 4.5

o 4 U E

._E 3

e 2.5 õ z 't.5 .! i iÉÉ¡d -.-*-*_- ';'_ÃÌ'r*' E 1 -,-*--; z 0.5 lndêx of Smallêst Eventg Dêtætabls i .1.3

0 0.0 0.5 1.0 r.5 2.0 2.5 3.0 3.5 4.0 4.5 s.o 5.5 6.0 Amplituds ofAdded Sinusoidal Compon6nt (uS)

Figure 25: sensitivity of Golden Boy's s-EID system to sinusoidal Excitation of St¡ain Gauge Channels

5.5

o 5 E 4.5

4 ù E ø.E s 2,5 -> õ 2 z

.N E z 0.5 l¡dsx olSmallsst Events Det¿itâbls = i.3

0 0.000 0.002 0.004 0.006 0.008 Amplitude ofAdd6d Sinusoldal Component (g)

Figure 26: sensitivity of Golden Boy's s-EID system to sinusoidal Excitation of Accelerometer Channels

ðt Table 3: Summary of EID Vs. S-EID System Sensitivity

St¡ucture Min. Strain Min. Strain Min. Accel. Min. Accel.

Name Event EID Event S-EID Event EID Event S-EID

Taylor Bridge 0.75pe 3.4¡L.e NA NA Portage Creek Bridge 72.5pe 70¡-re 0.00459 0.021s

Golden Boy I.25pe 4.75¡L.e 0.00209 0.075s

Figures 22 to 26 show that the s-EID system is less sensitive to events than the EID system. Notice that although the EID system showed only a slight ability to discriminate between events exhibiting difierent frequencies, the s- EID system is completely insensitive.

In some sense the s-EID system behaves purely as an aggregate sHM sensor channel power meter. The simplified approach of the S-EID system may be appropriate for use with the sHM systems of the Taylor Bridge, portage creek

Bridge and Golden Boy. It is important to remember that the s-EID system inherentìy assumes that a "mudane event,' is one which exhibits little or no ,,novel signal power on the sensor channels, and that a event', of interest is one which exhibits higher signal power levers. Although this may be a reasonable assumption for the test structures in this thesis, it may not be in general. one need only image a structure whose "mundane', state of existence exhibits non- zero power spectra on its sHM sensor channels and the s-EID approach would become useless. such a structure could be as simple and commonplace as a bridge under moderate 24-hour traffic.

Having explored two event identification systems, it may not be immediately apparent why the EID system is more sensitive than the s-EID system. It makes more sense if we consider the s-EID system in a neural computing context. The s-EID system can be thought of as a neural computing system

38 with a single learning unit located at the origin of the multi-dimensional neural computing space. The s-EID system's conceptual singre learning unit is not updated, and does not rearn. strictry speaking, the s-EID system is in no-way a neural computing system, but the anarogy is usefur for visualization purposes. F\rnctionally this description is accurate. Figure 27 shows conceptuary how the EID system characterizes the sHM system output in a noisy environment. Figure 2g shows how the s-EID system characterizes the same output. The s-EID system is not able to adapt to noise on the sensor channel outputs in any way. The EID system is perfectry capable

of characterizing a noise enverope for "mudane" sHM output states. The above conceptuaÌ comparison is not fair since it ignores the fact that the EID system performs a power spectral density estimation on the windows of sHM data. F\rrther experimentation would be required to completeiy and discretely characterize the costs and benefits of the power spectral density estimation and neural computing aspects of the EID system. The importance of the conceptual comparison is to illustrate the EID system's capability, and the S-EID system's inability to characterize noisy SHM outputs.

39 EID System Characterization Of Multi-Dimensional SHM System Output Space

Region Representing Mundane SHM System Output Statesl{ )

u Learning Units

Figure 27: EID Model of SHM System Output

40 S-EID System Characterization Of Multi-Dimensional SHM System Output Space

Region Representing Mundane SHM System

Figure 28: S-EID Model of SHM System Output

4t Table 4: Seismic Events at Portage Creek Bridge

Event Earthquake Event A Event Maximum Time (UTC) Time Time Duration Â Strain I 2003- 12-19 05:33 2003-12-18 23:13 19m 30s -1ls 13¡-te 2 2004-01-01 I5:12 2004-01-01 08:56 15m 40s -23s l0¡-re 2004F0+25 10:02 2004-04-25 04:55 6m 40s -19s 12¡te

5 seismic Events Affecting portage creek Bridge

This section discwses some events affecting the portage creek Bridge which are believed to be a result of seismic activity. T¿ble 4 summarizes some of the properties of these events. The second column shows the times at which seismic activity was recorded by the Ministry of ryansporation of British columbia, near the Portage creek Bridge. The third column shows the times at which activity was detected by the EiD system. The discrepancies between the time stamps are believed portage to be anomalies of the creek Bridge's data acquisition system. The duration of the events, and the maximum change in strain incurred by the bridge's strain gauges are shown to reference the strength of the events. The event duration was estimated by examining the frequency spectra of the sensor channels for activity around the times of the events.

42 PortÂgecreek, (2O0+t2-fB Zglsf)) + (O..jOæ . is) 25

x 15 ã 210o

ii .11 ll i: l; rtu{..¡n ¿-¡J.r:f 9,rl ${. a/++ ",a/]w.&.a ty"$?,,J-Þ-: È./.!itr-, *!*¿!da." iìu,i 0 "e_ o 128 258 3a4 512 S40 78A 69€ 1024 Seænds

Figure 29: Seismic Event 1 - Identified by EID System (Center peak)

Portagecræk, (200S12-18 2g:tgp3) + (0..51 I . j/32s) 38 StratnT_l c2 I 36 rlll

34 J l#,iii il.l "" il. 32 ¡ili ll lìi 'F so i ! ti e Jr i .9 28 ilr,i,ll i I 26 I

'{',ll,o q"ïfk/ yryutJ.lu*r"ffi ,,,,,t ¡ 22

20 o2468101214.t6 Secondg

Figure 30: Seismic Event 1 - Most Active Strain Gauge

43 Portagec@lç (20ø-t-1 8:4€ft) + (0..1029. 1s) 5

4.5

3.5

x3 E .E \ 2.5

f.5 å:

1 ¡i,,,i¡,|,|,',,ru'i'u.,irt4 l,*+',t-,',,',l,,.'çJJ|.irr¡,,,"W,nt¿+.i.',+ +

0.5

o 12A 256 384 512 e40 7æ 898 1024 Seænds

Figure 31: Seismic Event 2 - Identified by EID System

Portsgecræk, (200+t-1 8:56:15) + (0..s1.t . 1/32s) 34

30 c28 õ Þ o 826

20 0248810121416

Figure 32: Seismic Event 2 - Most Active Strain Gauge

44 PortagsQæk, . (20ff¡-4-25 4:470) + (0.. t 023 1 s) 14

g8 5 õ z

ilh¡i'È*{tÁtii14,p,, 0 0 128 258 384 512 640 768 89€ 1024 Seconds

Figure 33: Seismic Event 3 - Identified by EID System

Portsgecræk, (200¡¡4-25 4:5513) + (0..S1 I . 1/32s) 22 St¡sin8_l_cl ----.

c 16 'õ: I E14

'12

I 0248810121418 Seænd6

Figure 34: Seismic Event 3 - Most Active Strain Gauge

45 The EID system (in its present form) cannot distinguish between seismic and vehicular trafrc events. In order to ìocate the above events, the EID system output was examined near the times of known seismic events. Those events identified by the EID system closest in time to the known seismic events are assumed to represent Portage creek Bridge's response to seismic activity.

using the EID system to help locate known seismic events in an sHM data set is a minor accomplishment. In the futu¡e, further research may enable an sHM system to classify, as well as identify events. A successful mechanism for classification would obviate the need to cross-reference EID system output against the times of known seismic events-

46 6 Conclusions and F\rture Work

This thesis has shown that an unsupervised neural computing approach can

be very effecLive for event identification in sHM systems. The EID system described in this thesis is capable of identifying events in sHM data consisting of very small changes in sensor channel output values. In many cases the EID ,,sub-microstrain system is capable of identifying events.', The EID and s-EID system performance analysis showed that in some cases,

a simplified approach to event identification may be appropriate. Despite being

less sensitive, the s-EID system discussed in section 4 proved to be capable of

detecting many SHM events. In some cases, system simplicity may be more important than system sensitivity.

The importance of sensitvity adaptability in a noisy environment should not be underestimated. Despite the cost and complexity associated with the frequency-domain unsupervised neural computation approach outlined in this thesis, it may still be useful. A structurar engineer may not normally be interested in being able to identify "one microstrain events" versus only ,,ten microstrain events", but sHM system instalations offer diverse and varied sce_ narios.

one need only imagine an sHM site where key sensor locations are inaccessi- ,,insensitive', ble, and engineers are required to locate sensors in locations. one could also imagine sHM scena¡ios where the stiffness of the structure, or nature of the stimulus v/ere such that an sHM system event was represented by only very small deviations in sensor channel outputs. Signal levels associated with

sHM systems may vary greatly depending on circumstances. Noise levels asso- ciates with sHM system sensors are a given phenomena. If the circumstances surrounding an sHM system require enhanced sensitivit¡ an approach such as the one outlined by the EID system may be required.

47 This thesis has explored techniques for sHM which are adaptable, versatile and self-configuring. we have also shown that meaningful sHM data processing is possible without detaiied knowledge of the structure being monitored.

F-uture Work

Having explored and developed EID for sHM, a logical next step is to explore event classification. For example, an event classification system might be able to distinguish between a heavy truck, or an earthquake. F\rrther sub-classification possible. is also An event classification system might be able to distinguish between a fast-moving light truck and a srow-moving heavy truck.

Preliminary experimentation shows that neurar computing systems have po-

tential for effective event classification. Ironically, there is also a monotonous aspect to sHM events, in that a given structure will tend to react similarly, to similar stimulus at different instances in time. Experimentation using neural computation to classify sHM events indicates it is possible to classify sHM event at least according to their "strength." such classification may be immediately useful in implementing sHM systems which count sHM events to estimate levels of fatigue in civiì structures.

one ambitious long term goal of sHM system research is to develop ,,Red- Iight, Green-light" sHM systems. such a system would hypotheticaily monitor a structure with enough sophistication to make its own assessment of a structure's health. A "Green Light" assures structure owners that the structure is healthy, and a "Red Light" alerts ourners that there is a problem which needs attention. Although such systems will require much further research to develop, it is exciting to imagine a structure which can assess and report its own health, in detail, or in summary, to its owners.

The more we explore data processing for sHM systems, the more research opportunities present themselves.

48 References

[1] A. Mufti, Guidelines for stracturat Health Monitoring,ISlS canada, 2001

[2] s. Haykin, Neural Networks A comprehens,iue Found,atz,on, Macmillan col- lege Publishing Company Inc., 1gg4

[3] c. M. Bishop, Neural Networks for pattetn Recognition, oxford university Press, 1995

[4] s' c. Ahalt, A. K. Krishnamurthy, p. chen, D. E. Merton, "vector quantization using frequency-sensitive competitive-learning neural networks', , IEEE Intnl. Conf. on Systems Engineering, pp. 131-184, 19g9.

[5j D. K. McNeill, Ad,apti,ue vi,sual Representations for Autonomous Mobi.Ie Robots us'ing competitiue Learning Algorithms, ph.D. Dissertation, Dept.

of Electrical and computer Engineering, university of Manitoba, 1ggg.