Journal article
Measurements Selection for Bias Reduction in Structural Damage Identification
Journal of dynamic systems, measurement, and control, Vol.141(3), 031003
03/01/2019
DOI: 10.1115/1.4041505
Abstract
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. It is found 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.
Details
- Title: Subtitle
- Measurements Selection for Bias Reduction in Structural Damage Identification
- Creators
- Yuhang Liu - University of Wisconsin–MadisonShiyu Zhou - University of Wisconsin–MadisonYong Chen - University of IowaJiong Tang - University of Connecticut
- Resource Type
- Journal article
- Publication Details
- Journal of dynamic systems, measurement, and control, Vol.141(3), 031003
- Publisher
- ASME
- DOI
- 10.1115/1.4041505
- ISSN
- 0022-0434
- eISSN
- 1528-9028
- Language
- English
- Date published
- 03/01/2019
- Academic Unit
- Industrial and Systems Engineering
- Record Identifier
- 9984186583102771
Metrics
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