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Proceedings Paper

Tailored deterministic and stochastic excitations for structural health monitoring via evolutionary algorithms
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Paper Abstract

We have demonstrated that the parameters of a system of ordinary differential equations may be adjusted via an evolutionary algorithm to produce 'optimized' deterministic excitations that improve the sensitivity and noise robustness of state-space based damage detection in a supervised learning mode. Similarly, in this work we show that the same approach can select an 'optimum' bandwidth for a stochastic excitation to improve the detection capability of that same metric. This work demonstrates that an evolutionary algorithm can be used to shape or color noise in the frequency domain, such that improvement is seen in the sensitivity of the detection metric. Properties of the improved stochastic excitations are compared to their deterministic counterparts and used to draw inferences concerning a globally preferred excitation type for the model spring-mass system.

Paper Details

Date Published: 11 April 2007
PDF: 12 pages
Proc. SPIE 6532, Health Monitoring of Structural and Biological Systems 2007, 653210 (11 April 2007); doi: 10.1117/12.715319
Show Author Affiliations
Colin C. Olson, Univ. of California, San Diego (United States)
M. D. Todd, Univ. of California, San Diego (United States)


Published in SPIE Proceedings Vol. 6532:
Health Monitoring of Structural and Biological Systems 2007
Tribikram Kundu, Editor(s)

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