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Bridge & Structure Laboratory University of Tokyo 1 Structural health monitoring using Imote2 Tomonori Nagayama Assistant Professor University of Tokyo.

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Presentation on theme: "Bridge & Structure Laboratory University of Tokyo 1 Structural health monitoring using Imote2 Tomonori Nagayama Assistant Professor University of Tokyo."— Presentation transcript:

1 Bridge & Structure Laboratory University of Tokyo 1 Structural health monitoring using Imote2 Tomonori Nagayama Assistant Professor University of Tokyo 07/10/2009

2 Bridge & Structure Laboratory University of Tokyo 2 Wireless sensor components - functionality- The Imote2 has promising features. But not all the functionalities needed in SHM are provided in OS/HW. Hardware OS Middleware SHM applications RF CPU Memory Power Sensor/ actuator sensing 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 timesyncdata aggregation comm/networking

3 Bridge & Structure Laboratory University of Tokyo 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 t2 t3 Node1 clock Node2 clock Node3 clock T2 T3 +T3 +T2 Global time

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

5 Bridge & Structure Laboratory University of Tokyo 5 Time synchronization accuracy check Timestamps of receivers are examined Send packet Concept Time synchronization t3 Get globaltime Repeat n times Time synchronization t3

6 Bridge & Structure Laboratory University of Tokyo 6 Synchronized Sensing accuracy check Synchronization accuracy … Beacon Reply global time Time synchronization error < 150  s. Mostly < 20  s Difference in returned global time stamps

7 Bridge & Structure Laboratory University of Tokyo 7 t2+  T t3+  T 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. t1 t2 t3 Node1 clock Node2 clock Node3 clock T2 T3 +T3 +T2 Global time +T3+    T +T2+    T t1+  T

8 Bridge & Structure Laboratory University of Tokyo 8 Time synchronization drift check Difference among local clocks (    T )are examined Send packet Time synchronization t3 Concept Get T2 Get T3 Get T4 Repeat n times Get T2+    T 1 Get T3+    T 1 Get T4+    T 1 Get T2+    T 2 Get T3+    T 2 Get T4+    T 2

9 Bridge & Structure Laboratory University of Tokyo 9 Drift estimation … Beacon Reply offset  is almost constant over time  is almost constant over time Difference in clock rates can be as large as 50  s/sDifference in clock rates can be as large as 50  s/s Clock drift   T (  s) However, time synchronization of the nodes does not provide synchronized sensing.

10 Bridge & Structure Laboratory University of Tokyo 10 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 node1 “Start sensing” node2 node3 Sampling timing time Actual start t1t1 t2t2 t3t3  t 1 !=  t 2 !=  t 3

11 Bridge & Structure Laboratory University of Tokyo 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 Bridge & Structure Laboratory University of Tokyo 12 Resampling basics fs 1 fs target upsample filter downsample To eliminate aliasing components Resampling without distortion in signal

13 Bridge & Structure Laboratory University of Tokyo 13 Combination of resampling and linear interpolation upsample filter downsample What if we need data at these timing ? Linear interpolation

14 Bridge & Structure Laboratory University of Tokyo 14 Cross spectrum Synchronized sensing accuracy check Accuracy of synchronization among signals  Cross spectral densities among sensors have almost flat phase meaning accurately synchronized signals 1 degree at 100Hz  1/360/100 = 28  s synchronization error Fourier transform

15 Bridge & Structure Laboratory University of Tokyo 15 packet Reliable communication Redundant packet transmission  Packet retransmission: Same packets are transmitted more than once  Erasure code lost packets can be reconstructed To transferSend To transferSend Received Reconstruct xx Packet loss x Packet loss ReceivedReconstruct However burst loss may happen, then ? xx xx

16 Bridge & Structure Laboratory University of Tokyo 16 Reliable communication Acknowledgement based approach To transfer Send 13 ACK Reconstruct Received ACK Reliable but slow to transfer a large amount of data

17 Bridge & Structure Laboratory University of Tokyo 17 Reliable communication Acknowledgement based approach: fewer ACK packets To transfer …16 Send …16 Reconstruct ACK 15 is missing Reliable and fast to transfer a large amount of data 16 … 15 All received

18 Bridge & Structure Laboratory University of Tokyo 18 Efficient data aggregation Application specific knowledge is utilized to efficiently perform data aggregation

19 Bridge & Structure Laboratory University of Tokyo 19 Application specific knowledge -Natural Excitation Technique-  Definition:  Estimate: Correlation function ( Cross Spectrum Density estimation ) Natural Excitation Technique (NExT) “Correlation functions satisfies EOM for free vibration” Data compression through averaging 1/20 - 1/10 (nd = 10-20) Measurement Subsequently decomposed into modal vibrations

20 Bridge & Structure Laboratory University of Tokyo 20 Centralized data aggregation Correlation function estimation  Requires signals from 2 nodes  2 approaches  Centralized implementation O(N · n d · n s )  Distributed implementation Transmission

21 Bridge & Structure Laboratory University of Tokyo 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. Unicast Broadcast

22 Bridge & Structure Laboratory University of Tokyo 22 Correlation function estimation  Requires signals from 2 nodes  2 approaches  Centralized implementation O(N · n d · n s )  Distributed implementation O( N(n d +n s )) Distributed Data Aggregation Transmission Data transfer requirement is a primary factor for power consumption. Distributed implementation has an advantage Ex) N = 1024, n d =20, n s = 15 Centralized implementation  286,720 Distributed implementation  27,648 A reduction factor of 10.4 Data transfer requirement is a primary factor for power consumption. Distributed implementation has an advantage Ex) N = 1024, n d =20, n s = 15 Centralized implementation  286,720 Distributed implementation  27,648 A reduction factor of 10.4

23 Bridge & Structure Laboratory University of Tokyo 23 Integration of middleware services into applications Example1 Distributed Computing Strategy for SHM Example2 Railroad bridge vibration monitoring

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

25 Bridge & Structure Laboratory University of Tokyo 25 DCS implementation 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.

26 Bridge & Structure Laboratory University of Tokyo 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 Bridge & Structure Laboratory University of Tokyo 27 The SDLV method The damaged element 8 has small stress indicating damage. Threshold 0.3 Damaged element

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

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

30 Bridge & Structure Laboratory University of Tokyo 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 Bridge & Structure Laboratory University of Tokyo 31


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