Distributed structural health monitoring a cyber physical system approach
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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).

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Distributed Structural Health Monitoring A Cyber-Physical System Approach

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Distributed Structural Health MonitoringA 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)

  • “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


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


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


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


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


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


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


Energy Consumption

Evaluation


Energy Consumption

Evaluation


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.


Outline

  • Distributed Structural Health Monitoring

  • ART: Adaptive Robust Topology Control


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 dBm

-25 dBm

0 dBm

Advantages of Topology Control

Testbed Topology


... 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


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?


Is Dynamic Power Adaptation Necessary?

Link 110 -> 139


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


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?


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


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


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.


Acknowledgement

  • Computer Science: Greg Hackmann,Fei Sun, Octav Chipara

  • Structural Engineering: Nestor Castaneda, Shirley Dyke


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


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