Measurements Selection for Bias Reduction in Structural Damage Identification

[+] Author and Article Information
Yuhang Liu

Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, 3255 Mechanical Engineering, 1513 University Ave., Madison, WI 53706

Shiyu Zhou

Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, 1513 University Ave., Madison, WI 53706

Yong Chen

Department of Mechanical and Industrial Engineering, University of Iowa, Iowa City, IA 52242

Jiong Tang

Department of Mechanical Engineering, University of Connecticut, Storrs, Storrs, CT 06269

1Corresponding author.

ASME doi:10.1115/1.4041505 History: Received January 03, 2018; Revised September 10, 2018


Linearization of the eigenvalue problem has been widely used in vibration-based damage detection utilizing the change of natural frequencies. However, the linearization method introduces bias in the estimation of damage parameters. Moreover, the commonly employed regularization method may render the estimation different from the true underlying solution. These issues may cause wrong estimation in the damage severities and even wrong damage locations. Limited work has been done to address these issues. We find that particular combinations of natural frequencies will result in less biased estimation using linearization approach. In this paper, we propose a measurement selection algorithm to select an optimal set of natural frequencies for vibration-based damage identification. The proposed algorithm adopts L1- norm regularization with iterative matrix randomization for estimation of damage parameters. The selection is based on the estimated bias using the least square method. Comprehensive case analyses are conducted to validate the effectiveness of the method.

Copyright (c) 2018 by ASME
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