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Structural Health Monitoring of Truss Type Structures Using Statistical Approach

[+] Author Affiliations
Mahdi Saffari, Ramin Sedaghati

Concordia University, Montreal, QC, Canada

Ion Stiharu

Concordia Univiversity, Montreal, QC, Canada

Paper No. SMASIS2014-7486, pp. V001T05A002; 7 pages
  • ASME 2014 Conference on Smart Materials, Adaptive Structures and Intelligent Systems
  • Volume 1: Development and Characterization of Multifunctional Materials; Modeling, Simulation and Control of Adaptive Systems; Structural Health Monitoring; Keynote Presentation
  • Newport, Rhode Island, USA, September 8–10, 2014
  • Conference Sponsors: Aerospace Division
  • ISBN: 978-0-7918-4614-8
  • Copyright © 2014 by ASME


This paper proposes an effective statistical based vibration health monitoring technique using Auto Regressive (AR) parameters and Support Vector Machine (SVM) for truss type structures. The finite element method has been utilized to obtain acceleration response signals of a space truss structure under random excitations. The signals are then processed to extract their AR parameters as the feature vectors in which the AR parameters of the healthy structure are considered to be the reference baseline data. A Damage Index is then defined to be the standard deviation of the feature vectors from the baseline data. The proposed index provides an effective tool to detect the damage in the structure. It is shown that using only one sensor, it is still possible to accurately detect the damage.

To locate the damage, data classification technique based on Support Vector Machine (SVM) has been employed. It is shown that SVM can successfully classify different signals extracted from the structure. Finally extensive sensitivity analysis has been performed to study the effect of different parameter such as crack size, number of sensors and AR parameter numbers on the accuracy of detection and localization processes.

Copyright © 2014 by ASME



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