4.7 Article

Structural damage detection with limited input and output measurement signals

Journal

MECHANICAL SYSTEMS AND SIGNAL PROCESSING
Volume 28, Issue -, Pages 229-243

Publisher

ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2011.07.026

Keywords

Structural damage/fault detection; Unknown inputs; Extended Kalman estimator; Least-squares estimation; Benchmark problem; Substructure approach

Funding

  1. National Natural Science Foundation of China (NSFC) [51178406]
  2. China National High Technology Research and Development Program [2007AA04Z420]
  3. China National Education Ministry

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It is important but still challenging to detect structural damage with limited input and output measurement signals. In this paper, an algorithm is proposed for detecting structural damage with limited input and output measurement signals. The algorithm is based on sequential application of an extended Kalman estimator for the extended state vector of a structure and least-squares estimation of its unknown external excitations. Analytical recursive solutions for the identification of structural parameters and unknown excitations are derived. Such straightforward derivation and analytical solutions are not available in the previous literature. Structural damage can be detected from the degradation of the identified element stiffness. Numerical examples of detecting damage of some small size structural systems are used to demonstrate the performances of the proposed algorithm. Then, the algorithm is extended to detect structural damage of large size structural systems based on substructure approach. Inter-connection effect between adjacent substructures is considered by 'additional unknown inputs' to substructures. It is shown that the 'additional unknown inputs' can be estimated by the algorithm without the measurements of the substructure interface DOFs, which is superior to previous identification approaches. A numerical example of detecting structural damage of a large size truss illustrates the efficiency of the proposed algorithm. (C) 2011 Elsevier Ltd. All rights reserved.

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