
Proceedings Paper
Damage detection using vector auto-regressive modelsFormat | Member Price | Non-Member Price |
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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
Published in SPIE Proceedings Vol. 8695:
Health Monitoring of Structural and Biological Systems 2013
Tribikram Kundu, Editor(s)
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)
Gang Liu, Chongqing Univ. (China)
Michael Todd, Univ. of California, San Diego (United States)
Zhu Mao, 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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