Structural health monitoring using Imote2

1. Structural health monitoring using Imote2 Tomonori Nagayama Assistant Professor University of Tokyo 07/10/2009

2. Wireless sensor components -functionality- • The Imote2 has promising features. But not all the functionalities needed in SHM are provided in OS/HW. SHM applications • Following functionalities are provided as middleware services. Users can utilize them to assemble their own SHM applications • Time Synchronization • Synchronizaed sensing • Reliable communication • Efficient data aggregation • Others timesync data aggregation Middleware comm/networking sensing OS CPUMemory Sensor/actuator RF Power Hardware

3. Synchronization basics • Node synchronization • Nodes exchange packet and estimate local clock offsets • Time synchronization protocols • Reference Broadcast Synchronization (RBS), Timing-sync Protocol for Sensor Network (TPSN), Flooding Time Synchronization Protocol (FTSP) t1 Global time Node1 clock T2 Node2 clock t2 +T2 T3 Node3 clock t3 +T3

4. Time synchronization middleware • Based on Flooding Time Synchronization Protocol (FTSP) • By cascading, this synchronization works on a multihop network Append time stamp t1 Concept Send packet t3 Obtain reception time t2 Global time = local time + t1-t2+t3

5. Time synchronization Time synchronization Getglobaltime Getglobaltime t3 t3 Getglobaltime Getglobaltime Getglobaltime Getglobaltime Time synchronization accuracy check • Timestamps of receivers are examined Concept Send packet Repeat n times

6. Synchronized Sensing accuracy check • Synchronization accuracy Difference in returned global time stamps Beacon Reply global time … Time synchronization error < 150 ms. Mostly < 20ms

7. Synchronization basics -drift- • Drift • Due to difference in clock speed of each node, difference among local times changes (almost linearly) • Synchronization error accumulates as time passes after the last synchronization unless appropriate compensation is performed. Global time t1+DT t1 Node1 clock T2 Node2 clock t2+DT +T2+a2DT t2 +T2 T3 Node3 clock t3+DT +T3+a3DT t3 +T3

8. GetT2 GetT2+a2DT1 GetT2+a2DT2 GetT3 GetT3+a3DT1 GetT3+a3DT2 Get T4 Get T4+a4DT1 Get T4+a4DT2 Time synchronization drift check • Difference among local clocks (aiDT)are examined Time synchronization Concept Send packet t3 Repeat n times

9. a Drift estimation Clock drift However, time synchronization of the nodes does not provide synchronized sensing. Beacon Reply offset aDT (ms) … • a is almost constant over time • Difference in clock rates can be as large as 50 ms/s

10. Actual start Toward synchronized sensing • EVEN If a command to start sensing is issued at the same time, the execution timing is different • Sampling timing has individual difference “Start sensing” dt1 != dt2 != dt3 dt1 node1 dt2 node2 dt3 node3 Sampling timing time

11. Two approaches for synchronized sensing • Strict HW control of sampling timing • Sampling has high priority than other tasks. • No need for post processing • Other tasks are delayed. • Resampling based approach • Sensing starts at the approximately same time． • Resampling based on accurate timestamping • Less requirement on HW • Timestamp + Resampling + linear interpolation -> VERY accurate synchronized sensing is realized Strict HW control HW control Resample

12. Resampling basics Resampling without distortion in signal upsample downsample filter To eliminate aliasing components fs1 fstarget

13. Combination of resampling and linear interpolation upsample downsample filter What if we need data at these timing ? Linear interpolation

14. Synchronized sensing accuracy check Accuracy of synchronization among signals • Cross spectral densities among sensors have almost flat phase meaning accurately synchronized signals Fourier transform Cross spectrum 1 degree at 100Hz  1/360/100 = 28ms synchronization error

15. Redundant packet transmission Packet retransmission:Same packets are transmitted more than once Erasure codelost packets can be reconstructed Reliable communication To transfer Send 13 15 21 13+15+21 13 15 21 To transfer Send 13 15 21 13 15 21 13 15 21 Reconstruct Received x x x 13 15 21 13 15 21 13+15+21 Received Reconstruct Packet loss x x x x 13 15 21 13 15 21 13 15 21 Packet loss 13 packet However burst loss may happen, then ?

16. Acknowledgement based approach Reliable communication To transfer Send 13 15 21 15 13 15 21 ACK ACK ACK Reconstruct Received 13 15 21 21 13 15 Reliable but slow to transfer a large amount of data

17. Acknowledgement based approach: fewer ACK packets Reliable communication To transfer Send 13 15 21 16 15 13 15 21 …16 ACK 15 is missing All received … Reconstruct 13 15 21 16 15 13 15 21 …16 Reliable and fast to transfer a large amount of data

18. Efficient data aggregation Application specific knowledge is utilized to efficiently perform data aggregation

19. Measurement Natural Excitation Technique (NExT) “Correlation functions satisfies EOM for free vibration” Correlation function Application specific knowledge -Natural Excitation Technique- • Definition: • Estimate: Subsequently decomposed into modal vibrations （Cross Spectrum Density estimation ) Data compression through averaging1/20－1/10(nd = 10-20)

20. Centralized data aggregation Correlation function estimation • Requires signals from 2 nodes • 2 approaches • Centralized implementation O(N·nd·ns) • Distributed implementation Transmission

21. Packet transfer • Broadcast and unicast • Broadcast: 1-to-”others in the range” • Unicast: 1-to-1 • Specify the destination by node ID • Basically broadcast, but others ignore. Broadcast Unicast

22. Distributed Data Aggregation Correlation function estimation • Requires signals from 2 nodes • 2 approaches • Centralized implementation O(N·nd·ns) • Distributed implementation O( N(nd+ns)) Data transfer requirement is a primary factor for power consumption. Distributed implementation has an advantage Ex) N = 1024, nd=20, ns= 15 Centralized implementation  286,720 Distributed implementation  27,648 A reduction factor of 10.4 Transmission

23. Integration of middleware services into applications • Example1 Distributed Computing Strategy for SHM • Example2 Railroad bridge vibration monitoring

24. Distributed Computing Strategy for SHM DCS flow chart （常時微動計測を仮定） 1. Vibration measurement ambient vibration measurement • 2. Modal analysisin each cluster • OutPut: Natural frequency, Mode shape, A,C matrices • Method: NExT, ERA Cluster formation Sensing NExT • 3. Damage assessmentin each community • Output: Damage location • Method: Stochastic damage locating vector ERA SDLV • 4. Synthetic judgment among cluster heads • Output: Damage location • Method: DCS logic DCS logic

25. middleware Reliable communication Synchronized sensing Efficient data aggregation Numerical library FFT SVD Eigensolver sort Static stress analysis (a part of damage detection) All the tasks are predefined. Once parameters are injected to the network, the Imote2s autonomously perform damage identification. DCS implementation

26. Experimental Verification • Ten Imote2s, 3 clusters autonomously monitor the 3D truss scale model • Longitudinal & vertical measurements • Damage simulated by an element with a small cross-section is localized by Imote2s 53% cross section reduction

27. Damaged element The SDLV method • The damaged element 8 has small stress indicating damage. Threshold0.3

28. Railroad bridge monitoring application • Detailed modal analysis of viaducts from ambient vibration is non-trivial ⇒ exploit traffic vibration • Node wakeup based on train schedule • Extract time traffic vibration • Data processing Wakeup Sensing • After train passage natural frequencieslinear damage • During train passage： Vibration amplitude abnormal vibration Coherence functionnon-linearity Leaf node Signal extraction Cluster head Modal identification Amplitude levelcoherence function Leaf node Report to BS Leaf node

29. Service-Oriented Architecture Service-Oriented Architecture (SOA) in the SHM Toolkit simplifies SHM software development Applications are comprised of manageable, modular servicesthat exchange data in a common format The middleware framework connects the services by providing communication and coordination SDLV SHM Application Application Services Numerical Services Foundation Services

30. SHM Toolkit Contents • Foundation services • Universal sensing • Time synchronization • Reliable communication • Numerical library • Application services • Correlation function estimation (CFE) • Eigensystem Realization Algorithm (ERA) • Stochastic Damage Load Vector (SDLV) • Stochastic Subspace Identification (SSI) • Synchronized sensing • Test applications, tools and utilities • Radio & antenna testing • Data acquisition (local and remote) • Test applications for each component of the toolkit

31. http://shm.cs.uiuc.edu