Improving Excitations for Active Sensing in Structural Health Monitoring via Evolutionary Algorithms

Active excitation is an emerging area of study within the field of structural health monitoring whereby prescribed inputs are used to excite the structure so that damage-sensitive features may be extracted from the structural response. This work demonstrates that the parameters of a system of ordinary differential equations may be adjusted via an evolutionary algorithm to produce excitations that improve the sensitivity and robustness to extraneous noise of state-space based damage detection features extracted from the structural response to such excitations. A simple computational model is used to show that significant gains in damage detection and quantification may be obtained from the response of a spring-mass system to improved excitations generated by three separate representative ordinary differential equation systems. Observed differences in performance between the excitations produced by the three systems cannot be explained solely by considering the frequency characteristics of the excitations. This work demonstrates that the particular dynamic evolution of the excitation applied to the structure can be as important as the frequency characteristics of said excitation if improved damage detection is desired. In addition, the implied existence of a globally optimum excitation (in the sense of improved damage assessment) for the model system is explored.

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