Cyber-Physical Codesign of Distributed Structural Health Monitoring With Wireless Sensor Networks

1. Cyber-Physical Codesign of Distributed Structural Health • Monitoring With Wireless Sensor Networks • Gregory Hackmann*, Weijun Guo*, Guirong Yany, Chenyang Lu*, Shirley Dykey • *Department of Computer Science and Engineering, • Washington University in St. Louisy School of Mechanical Engineering, Purdue University • Presented By: • Ayush Khandelwal

2. About the Authors: • Gregory Hackmann:Postdoctoral Research Assistant, Washington University in St. Louis .Department of Computer Science and Engineering • WeijunGuo: Research Associate at North Carolina State Univ. • GuirongYany: Researcher in Mechanical Engineering, Purdue University • Chenyang Lu: Professor of Computer Science and Engineering ,Washington University in St. Louis • Shirley J. Dyke :Purdue University, Professor of Mechanical and Civil Engineering

3. Acknowledgements: This work is supported by NSF NeTS-NOSS Grant CNS-0627126 and CRI Grant CNS-0708460

4. Content: • Abstract • Introduction • Previous/Related Works • Damage localization approach • Distributed architecture • Multi-Level Damage Localization • Network Hierarchy • Enhanced FDD • Implementation • Hardware Platform • Software Platform • Evaluation • Cantilever Beam • Truss • Conclusion

5. Abstract: Our deteriorating civil infrastructure faces the critical challenge of long-term structural health monitoring for damage detection and localization. In contrast to existing research that often separates the designs of wireless sensor networks and structural engineering algorithms, this paper proposes a cyber-physical co-design approach to structural health monitoring based on wireless sensor networks. Our approach closely integrates (1) flexibility-based damage localization methods that allow a tradeoff between the number of sensors and the resolution of damage localization, and (2) an energy-efficient, multi-level computing architecture specially designed to leverage the multi-resolution feature of the flexibility-based approach. The proposed approach has been implemented on the Intel Imote2 platform. Experiments on a physical beam and simulations of a truss structure demonstrate the system's efficacy in damage localization and energy efficiency.

6. Lets get started… • Deteriorating Civil Infrastructures • Problems with sensors in Wired Technology • Growth in Wireless Sensor Networks (WSN’s ) • Problems With Centralized Systems viz. High latency and high Energy consumption. • Best Solution : Usage of CPS to provide Structural Health Monitoring using de-centralized systems.

7. Related Works.. • UC Berkley Project to monitor Golden Gate Bridge • Clarkson’s University Implementation on a bridge structure In New York. • Problems: • Limited data Collection in a time frame. • Inadequacy for time constraint events due to large time for data analyzation and collection. • Solution: • Usage of Distributed Approach based on Damage Localization

8. Damage localization approach : • Physical Aspect using Flexibility based Algorithm • Two stages of Flexibility Algorithm • Baseline Structural Model Identification (Fb) • Repeatedly collecting data over the passage of time (F)

10. The data flow of a traditional flexibility-based method

11. Methods of Flexibility-Based Algorithm : • Angles-Between-String-and-Horizon flexibility-based method (ASHFM) • Axial Strain flexibility-based method (ASFM) • Formula for difference in matrix for ASHFM: • ∆F = |Fb – F| • Fbis the flexibility matrix on baseline • F is computed the newly computed flexibility matrix • ∆F is damage matrix

12. Damage Indicator:

13. Distributed Architecture: • Described method is good for Centralized networks. But is not energy efficient and good for localization • Multi-Level damage Localization: • Uses multi level search • If damage not found return nodes to sleep • If found, Multi-level search is performed and identify adjacent sensors. • Key feature: doesn’t activate all sensors at once.

15. Damage localization results on the cantilever beam

16. Network Hierarchy: • Roles of nodes: • Cluster Member • Cluster Head • Base Station • Accelerometers are used to collect information.

18. Enhanced FDD: Problem: High number of outputs from CSD and SVD which is not energy efficient Solution: Peak Picking Routine in FDD stage which allows each node to independently identify these P natural frequencies solely from local data.

19. Implementation: • Hardware: • Imote2 wireless Sensor • PXA271 Xscale processor • 256kb SRAM, 32 MB SDRAM • Dynamically clocking from 13-416 MHz • Modular stackable platform providing add-on accelerometers

20. Software: • Components: • nesC Programming Language • TinyOSOperating System • ISHM’s ReliableComm • DistributedDataAcquireApp • The Two stage Search • Usage of TDMA for time synchronization of collected samples

22. Evaluation/ Deployment : • On Cantilever Beam (using ASHFM) • On Truss (using ASFM)

23. Cantilever Beam Deployement:

24. Damage localization results on the cantilever beam

25. Truss Deployement: 1.Damage Localization:

26. 2. Energy Consumption:

27. Conclusion: • Flexibility-based structural engineering methods that can localize damages at different resolution and costs • An efficient, multi-level computing architecture that leverage on the multi-resolution feature of flexibility-based methods