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

Colin C. Olson; M. D. Todd

doi:10.1117/12.715319

11 April 2007 Tailored deterministic and stochastic excitations for structural health monitoring via evolutionary algorithms

Colin C. Olson, M. D. Todd

Author Affiliations +

Proceedings Volume 6532, Health Monitoring of Structural and Biological Systems 2007; 653210 (2007) https://doi.org/10.1117/12.715319
Event: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2007, San Diego, California, United States

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.

Citation Download Citation

Colin C. Olson and M. D. Todd "Tailored deterministic and stochastic excitations for structural health monitoring via evolutionary algorithms", Proc. SPIE 6532, Health Monitoring of Structural and Biological Systems 2007, 653210 (11 April 2007); https://doi.org/10.1117/12.715319

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