Applying Sensitivity Analysis in Structure Damage Identification

Structure health monitoring aims to detect the nature of structure damage by using a network of sensors, whose sensor signals are highly correlated and mixed with noise, it is difficult to identify direct relationship between sensors and abnormal structure characteristics. In this study, we apply sensor sensitivity analysis on a structure damage identifier, which integrates independent component analysis (ICA) and support vector machine (SVM) together. The approach is evaluated on a benchmark data from University of British Columbia. Experimental results show sensitivity analysis not only helps domain experts understand the mapping from different location and type of sensors to a damage class, but also significantly reduce noise and improve the accuracy of different level damages identification.

Authors and Affiliations

1. School of Computer Science and Technology, Wuhan University of Technology, Wuhan, Hubei, 430070, China

   Huazhu Song & Luo Zhong

2. School of Computer Science, Wuhan University, Wuhan, Hubei, 430072, China

   Bo Han

3. Dept. of Computer and Information Science, Temple University, Philadelphia, PA, 19122, U.S.A.

   Bo Han

Editors and Affiliations

1. School of Electrical and Electronic Engineering, Nanyang Technological University,, Block S1, Nanyang Avenue, 639798, Singapore

   Lipo Wang

2. Life Science Research Center, School of Electronic Engineering, Xidian University,, 710071, Xi’an, Shaanxi, China

   Licheng Jiao

3. School of Electrical and Electronic Engineering, Xidian University, 710071, Xi’an, China

   Guanming Shi

4. School of Information Technology and Electrical Engineering, The University of Queensland, 4072, Brisbane, Queensland, Australia

   Xue Li

5. College of Mathematics and Information Science, Hebei Normal University, 050016, Shijiazhuang, Hebei, P.R. China

   Jing Liu

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