Cyber-Physical Codesign of Distributed Structural Health Monitoring With Wireless Sensor Networks
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Cyber-Physical Codesign of Distributed Structural HealthMonitoring 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
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About the Authors:
• Gregory Hackmann :Postdoctoral Research Assistant, Washington University in St. Louis .Department of Computer Science and Engineering
• Weijun Guo: Research Associate at North Carolina State Univ.
• Guirong Yany: 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
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Acknowledgements:
This work is supported by NSF NeTS-NOSS Grant CNS-0627126 and CRI Grant CNS-0708460
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Content:
1. Abstract2. Introduction3. Previous/Related Works4. Damage localization approach5. Distributed architecture
1. Multi-Level Damage Localization2. Network Hierarchy3. Enhanced FDD
6. Implementation1. Hardware Platform2. Software Platform
7. Evaluation1. Cantilever Beam2. Truss
8. Conclusion
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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 networksand 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 localizationmethods 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.
Abstract:
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• 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.
Lets get started…
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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
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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)
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The data flow of a traditional flexibility-based method
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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|
Fb is the flexibility matrix on baselineF is computed the newly computed flexibility matrix∆F is damage matrix
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Damage Indicator:
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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.
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Damage localization results on the cantilever beam
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Network Hierarchy:
Roles of nodes:
• Cluster Member • Cluster Head• Base Station
Accelerometers are used to collect information.
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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.
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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
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Software:
Components:
• nesC Programming Language
• TinyOS Operating System
• ISHM’s ReliableComm
• DistributedDataAcquireApp
The Two stage Search
Usage of TDMA for time synchronization of collected samples
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Evaluation/ Deployment :
• On Cantilever Beam (using ASHFM)
• On Truss (using ASFM)
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Cantilever Beam Deployement:
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Damage localization results on the cantilever beam
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Truss Deployement:
1.Damage Localization:
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2. Energy Consumption:
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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