1 / 25

Distributed Structural Health Monitoring A Cyber-Physical System Approach

Distributed Structural Health Monitoring A Cyber-Physical System Approach. Chenyang Lu Department of Computer Science and Engineering. Outline. Distributed Structural Health Monitoring ART: Adaptive Robust Topology Control. Structural Health Monitoring (SHM).

evania
Download Presentation

Distributed Structural Health Monitoring A Cyber-Physical System Approach

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Distributed Structural Health MonitoringA Cyber-Physical System Approach Chenyang Lu Department of Computer Science and Engineering

  2. Outline • Distributed Structural Health Monitoring • ART: Adaptive Robust Topology Control

  3. Structural Health Monitoring (SHM) • “More than 26%, or one in four, of the nation's bridges are either structurally deficient or functionally obsolete.” [ASCE 2009] • Detect and localize damages to structures • Wireless sensor networks can monitor at high temporal and spatial granularities • Key Challenges • Computationally intensive • Resource and energy constraints • Long-term monitoring

  4. Existing Approaches • Centralized approach: stream raw sensor data to base station for processing. • Example: Golden Gate Bridge monitoring project [UCB] • Nearly 1 day to collect enough data for one computation • Lifetime of 10 weeks w/4 x 6V lantern battery • Observations • Too much sensor data to stream to the base station • Damage detection is too complex to run entirely on sensors • Separate designs of SHM algorithm and sensor networks

  5. Our Approach • Distributed Architecture • Performs part of computation on sensor nodes • Send partial (smaller) results to base station • Base station completes computation • Cyber-Physical Co-design • Select an SHM algorithm that can be partitioned into components • Optimal partition of the SHM algorithm between sensor nodes and base station Raw Data Partial Results

  6. Damage Localization AlgorithmDamage Localization Assurance Criterion (DLAC) • Use vibration data to identify structure’s natural frequencies. • Match natural frequencies with models of healthy and damaged structures to localize damage. • Important: partition between sensors and the base station. • Minimize energy consumption • Subject to resource constraints Raw Data Partial Results

  7. D Integers (1) FFT D: # of samples P: # of natural freq. (D » P) D Floats (3a) Coefficient Extraction (2) Power Spectrum 5*P Floats D/2 Floats (3) Curve Fitting (3b) Equation Solving P Floats Healthy Model Damaged Location (4) DLAC Data Flow Analysis DLAC Algorithm

  8. 4096 bytes (1) FFT D: 2048 P: 5 Integer: 2 bytes Float: 4 bytes 8192 bytes (3a) Coefficient Extraction (2) Power Spectrum Effective compression ratio of 204:1 100 bytes 4096 bytes (3) Curve Fitting (3b) Equation Solving 20 bytes Healthy Model Damaged Location (4) DLAC Data Flow Analysis DLAC Algorithm

  9. Evaluation: Truss • 5.6 m steel truss structure at UIUC • 14 0.4m-long bays, sitting on four rigid supports • 11 Imote2s attached to frontal pane Damage correctly localized to third bay

  10. Energy Consumption Evaluation

  11. Energy Consumption Evaluation

  12. Summary • Cyber-physical co-design of a distributed SHM system • Reduces energy consumption by 71% • Implemented on iMote2 platform using <1% of memory • Effectively localized damage on two physical structures G. Hackmann, F. Sun, N. Castaneda, C. Lu, and S. Dyke, A Holistic Approach to Decentralized Structural Damage Localization Using Wireless Sensor Networks, RTSS 2008.

  13. Outline • Distributed Structural Health Monitoring • ART: Adaptive Robust Topology Control

  14. Topology Control • Goal: reduce transmission power while maintaining satisfactory link quality • But it’s challenging: • Links have irregular and probabilistic properties • Link quality can vary significantly over time • Human activity and multi-path effects in indoor environments • Most existing solutions are based on ideal assumptions • Contributions: • Insights from empirical study in an office building • ART: robust topology control designed based on insights

  15. -15 dBm -25 dBm 0 dBm Advantages of Topology Control Testbed Topology

  16. ... but have modest performance @ -5 dBm Insight 1: Transmission power should be set on a per-link basis to improve link quality and save energy. 3 of 4 links fail @ -10 dBm ... Is Per-Link Topology Control Beneficial? Impact of TX power on PRR

  17. Low signal strength High contention Insight 2:Robust topology control algorithms must avoid increasing contention under heavy network load. What is the Impact of Transmission Power on Contention?

  18. Is Dynamic Power Adaptation Necessary? Link 110 -> 139

  19. Insight 3: Robust topology control algorithms must adapt their transmission power in order to maintain good link quality and save energy. Can Link Stability Be Predicted? Long-Term Link Stability

  20. Are Link Indicators Robust Indoors? • Two instantaneous metrics are often proposed as indicators of link reliability: • Received Signal Strength Indicator (RSSI) • Link Quality Indicator (LQI) • Can you pick an RSSI or LQI threshold that predicts whether a link has high PRR or not?

  21. RSSI threshold = -85 dBm, PRR threshold = 0.9 4% false positive rate 62% false negative rate RSSI threshold = -84 dBm, PRR threshold = 0.9 66% false positive rate 6% false negative rate Insight 4: Instantaneous LQI and RSSI are not robust estimators of link quality in all environments. Are Link Indicators Robust Indoors? Links 106 -> 129 &104 -> 105

  22. Summary of Insights • Set transmission power on a per-link basis • Avoid increasing contention under heavy network load • Adapt transmission power online • LQI and RSSI are not robust estimators of link quality

  23. ARTAdaptive and Robust Topology control Designed based on insights from empirical study • Adjusts each link’s power individually • Detects and avoids contention at the sender • Tracks link qualities in a sliding window, adjusting transmission power at per-packet granularity • Does not rely on LQI or RSSI as link quality estimators • Is simple and lightweight by design • 392B of RAM, 1582B of ROM, often zero network overhead G. Hackmann, O. Chipara, and C. Lu, Robust Topology Control for Indoor Wireless Sensor Networks, SenSys 2008.

  24. Acknowledgement • Computer Science: Greg Hackmann,Fei Sun, Octav Chipara • Structural Engineering: Nestor Castaneda, Shirley Dyke

  25. For More Information • http://www.cse.wustl.edu/~lu/ • Structural Monitoring: http://www.cse.wustl.edu/~lu/shm/ • ART: http://www.cse.wustl.edu/~lu/upma.html

More Related