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Proceedings Paper

Damage detection using vector auto-regressive models
Author(s): Zongming Huang; Gang Liu; Michael Todd; Zhu Mao
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Paper Abstract

This study presents a damage detection method for transmission towers based on vector auto-regressive (VAR) models. The vibration signals obtained from both baseline and unknown conditions of the structure are divided into multiple data segments, respectively, and each segment is then modeled as a VAR time series. The diagonal elements of the VAR coefficient matrices series are extracted, and that vector’s Mahalanobis distance (MD) is used as a damage-sensitive feature. At the sensor locations where damage is introduced, the mean and variance of MD distribution will change from their values under baseline condition. Thus, the area under a receiver operating characteristic (ROC) curve and deflection coefficient of MD distribution are used as the decision metric for damage detection, localization, and severity. The method’s effectiveness is assessed on a 6 degree-of-freedom mass-spring simulation system and a transmission tower model. The results confirm the high potential and effectiveness of this method for data-driven damage assessment.

Paper Details

Date Published: 17 April 2013
PDF: 10 pages
Proc. SPIE 8695, Health Monitoring of Structural and Biological Systems 2013, 86953E (17 April 2013); doi: 10.1117/12.2012248
Show Author Affiliations
Zongming Huang, Chongqing Univ. (China)
Gang Liu, Chongqing Univ. (China)
Michael Todd, Univ. of California, San Diego (United States)
Zhu Mao, Univ. of California, San Diego (United States)


Published in SPIE Proceedings Vol. 8695:
Health Monitoring of Structural and Biological Systems 2013
Tribikram Kundu, Editor(s)

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