Structural Health Monitoring. CSE 520S Fall 2011

Transcription

1 Structural Health Monitoring CSE 52S Fall 211

2 Structural Health Monitoring (SHM) Problem: detect and localize damage to a structure Wireless sensor networks (WSNs) monitor at unprecedented temporal and spacal granularices Key Challenges: Long- term monitoring Rapid on- demand analysis Resource and energy constraints 2

3 Centralized Designs Wisden [Xu, SenSys 24] Services for reliable transmission of raw data Golden Gate Bridge [Kim, IPSN 27] 46- hop network deployed along Golden Gate Bridge BriMon [Chebrolu, MobiSys 28] Trains as data mules Torre Aquila [CerioZ, IPSN 29] Heterogeneous sensors, most with low data rate Primarily focus on data transport issues 3

4 Decentralized Design Principles Raw sensor data is too large to stream back to the base stacon Damage deteccon is too complex to run encrely onboard the sensors Raw Data SoluCon: decentralized codesign Select an algorithm which can be run parcally on the motes Send back (smaller!) parcal results to the base stacon to complete computacon 4

5 Decentralized System Evolution Damage LocalizaCon Assurance Criterion (DLAC) No collaboracon needed among nodes => lightweight network architecture Some limitacons in damage deteccon Flexibility- Based Methods CollaboraCon among nodes => more complex architecture, but more robust damage localizacon Even more energy savings through sensor seleccon 5

6 Damage Localization Assurance Criterion (DLAC) Collect vibracon data and use to idencfy structure s natural frequencies [Messina, J. Sound and Vibra:on, 1998] Signature of structure s health Several traits useful for a decentralized system No data exchanged among nodes IniCal stages are computaconally inexpensive Later stages have much smaller inputs (typically <1% of inical data set) 6

7 Amplitude!5!1!15! Time(s) Vibration Data Input Damage LocalizaCon Algorithm 7

8 Amplitude(dB) ! Frequency (Hz) FFT + Power Spectrum Analysis Damage LocalizaCon Algorithm 8

9 "%! "#! )*+,-./,12-34 "!! (! '! %! #!!!#!.! "! #! $! %! &! 6-,73,819.:;<= Curve Fitting Damage LocalizaCon Algorithm 9

10 DLAC A mathemaccal model of the structure is created offline Used to predict effect of structural damage on natural frequencies Natural frequency data input: Healthy structure Healthy model Model damaged at different discrete locacons (Possibly) damaged structure 1

11 1.9 Highest correlacon to damage at LocaCon Element Position DLAC Output Damage LocalizaCon Algorithm 11

12 D Integers (1) FFT D Floats D: # of samples P: # of natural freq. (D» P) (2) Power Spectrum D/2 Floats (3) Curve FiZng Healthy Model (4) DLAC P Floats Damaged LocaCon Data Flow Analysis Damage LocalizaCon Algorithm 12

13 496 Bytes (1) FFT D: 248 P: Bytes Integer: 2 bytes Float: 4 bytes (2) Power Spectrum EffecCve compression raco of 24:1 496 Bytes (3) Curve FiZng 2 Bytes Healthy Model (4) DLAC Damaged LocaCon Data Flow Analysis Damage LocalizaCon Algorithm 13

14 Implementation Hardware plaoorm: Intel/Crossbow Imote2 + ITS4 sensor board MHz XScale CPU 32 MB ROM, 32 MB SDRAM CC compliant radio 3- axis accelerometer on sensor board Sosware plaoorm: TinyOS KB ROM, 71 KB RAM 14

15 Evaluation: Truss 5.6 m steel truss structure at UIUC Fourteen.4 m long bays, sizng on four rigid supports 11 Imote2s atached to frontal pane 1 DLAC WS #32 1 DLAC WS #45 1 DLAC WS #67 1 DLAC WS #28 1 DLAC WS #35 1 DLAC WS #75.9 X = 3 Y = X = 3 Y = X = 3 Y = X = 3 Y = X = 3 Y = X = 3 Y = Damage correctly localized to.1 third.1 bay.1.1 Truss Frontal Panel Wireless Sensor Truss Central Bay Position Truss Central Bay Position Truss Central Bay Position Truss Central Bay Position Truss Central Bay Position Truss Central Bay Position 15

16 Centralized Decentralized Sampling ComputaCon CommunicaCon Latency (ms) Latency EvaluaCon 16

17 Centralized Decentralized Sampling ComputaCon CommunicaCon Energy consump3on (J) Energy Consumption EvaluaCon 17

18 DLAC: Findings Onboard processing reduces latency by 66% and energy consumpcon by 71% EffecCvely localized damage to discrete locacons on two structures Results indicate the power of holiscc energy management G. Hackmann, F. Sun, N. Castaneda, C. Lu, and S. Dyke, A HolisCc Approach to Decentralized Structural Damage LocalizaCon Using Wireless Sensor Networks, RTSS,

19 Flexibility- Based Methods Structures flex slightly when a force is applied Structural weakening => decreased scffness Flexibility acts as a signature of the structure s health Two flexibility- based methods of interest Beam- like structures: Angles- Between- String- and- Horizon flexibility method (ASHFM) [Duan, J. Structural Engineering and θ Mechanics 9] Truss- like structures: Axial Strain flexibility method (ASFM) [Yan, J. Smart Structures and Systems 9] 19

20 Network Architecture Sensors form physically- colocated groups Group members collect raw vibracon data and process into power spectrum data Group leaders collect corresponding power spectrum data from children, correlacng into modal parameters (natural frequencies + mode shapes) Group Member Group Leader Group Member Base Sta3on Group Member Group Leader Group Member Group Member 2

21 Network Architecture Base sta3on collects modal parameters from group leaders, completes processing into structural flexibility Output is compared against baseline output from healthy structure Differences in flexibility can be used to detect and localize damage Group Member Group Leader Group Member Base Sta3on Group Member Group Leader Group Member Group Member 21

22 Standard Data Flow Group Member Group Leader Base StaCon 2 x D ints D floats Sensing FFT Power Spectrum Cross Spectral Density D matrices Singular Value DecomposiCon Flexibility D: # of samples P: # of natural freq. (D» P) D floats P natural frequencies + mode shapes 22

23 Enhanced Distributed Data Flow Group Member 2 x D ints D floats Sensing FFT Power Spectrum D floats Peak Picking P floats Group Leader Cross Spectral Density P matrices Singular Value DecomposiCon Base StaCon Flexibility D: # of samples P: # of natural freq. (D» P) P natural frequencies + mode shapes 23

24 Multi- Resolution Damage Localization Under ASHFM and ASFM, only a handful of sensors are needed to detect damage As more sensors are added, localizacon gets more fine- grained Significant energy savings by exploicng localized nature of flexibility- based approach 24

25 Evaluation: Simulated Truss SimulaCon of UIUC truss structure Simulated sensor data generated in MATLAB and injected into live applicacon using fake sensor driver Intact data set: no damages Damaged data set: three members reduced on les side of truss, four on right side Result: Level 1 idencfied damage on both halves of truss; Level 2 localized damage correctly to all seven members 25

26 Evaluation: Simulated Truss Codesigned architecture reduces communicacon latency from escmated 87 s to.21 s 78.9% of energy atributable to synchronizacon and sensing Compare to theoreccal supply of 2,25 J from 3x AAA bateries G. Hackmann, W. Guo, G. Yan, C. Lu, and S. Dyke, Cyber- Physical Codesign of Distributed Structural Health Monitoring With Wireless Sensor Networks, ICCPS, 21. Group Member SynchronizaCon 12.1 J Sensing 23. J ComputaCon 9.28 J CommunicaCon.8 J Group Leader SynchronizaCon 16.2 J Sensing 21.2 J ComputaCon 8.52 J CommunicaCon.76 J 26

27 Image source: Zhuoxiong Sun, Purdue University Preliminary Test Full- Scale Truss 27

28 Test Results: Full- Scale Truss Two levels of damage localizacon Level 1: localized damage to bay 9 Level 2: localized damage to element 42 AS Flexibility Damage Indicator 2 x Truss Element Number 28

29 Conclusion Codesign approach integrates two SHM methods with efficient distributed compucng architectures Mul:- level search strategy only accvates sensors in area of interest; many sensors remain asleep Shown to localize damage to real beam and truss structures Long- term goal: a general codesign framework for integrated sensing and control 29

30 Papers G. Hackmann, F. Sun, N. Castaneda, C. Lu, and S. Dyke, A HolisCc Approach to Decentralized Structural Damage LocalizaCon Using Wireless Sensor Networks, IEEE Real- Time Systems Symposium (RTSS'8), December 28. G. Hackmann, W. Guo, G. Yan, C. Lu and S. Dyke, Cyber- Physical Codesign of Distributed Structural Health Monitoring with Wireless Sensor Networks, ACM/IEEE InternaConal Conference on Cyber- Physical Systems (ICCPS'1), April 21. 3

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32 Evaluation: Cantilever Beam 2.75 m x 7.6 cm x.6 cm steel beam in Structural Control and Earthquake Engineering Lab Damage simulated at three locacons by ataching a steel bar 7 Imote2s atached at equidistant locacons 1 DLAC WS1 1 DLAC WS2 1 DLAC WS3 1 DLAC WS4 1 DLAC WS5 1 DLAC WS6 1 DLAC WS m Wireless Sensor Damage Location X = 5 Y.9 =.94 X = 5 Y = X = 5 Y = X = 5 Y = X = 5 Y = X = 5 Y = X = 5 Y = m Damage correctly localized in all three trials m.66 m 1 2 Element Position 1 2 Element Position 1 2 Element Position 1 2 Element Position 1 2 Element Position 1 2 Element Position 1 2 Element Position 32