4.7 Article

Optimal selection of autoregressive model coefficients for early damage detectability with an application to wind turbine blades

Journal

MECHANICAL SYSTEMS AND SIGNAL PROCESSING
Volume 70-71, Issue -, Pages 557-577

Publisher

ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2015.09.007

Keywords

Autoregressive models; Damage detection; Hypothesis testing; Optimal feature selection; Time series; Wind turbine

Funding

  1. Lloyd's Register Foundation

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Data-driven vibration-based damage detection techniques can be competitive because of their lower instrumentation and data analysis costs. The use of autoregressive model coefficients (ARMCs) as damage sensitive features (DSFs) is one such technique. So far, like with other DSFs, either full sets of coefficients or subsets selected by trial-and-error have been used, but this can lead to suboptimal composition of multivariate DSFs and decreased damage detection performance. This study enhances the selection of ARMCs for statistical hypothesis testing for damage presence. Two approaches for systematic ARMC selection, based on either adding or eliminating the coefficients one by one or using a genetic algorithm (GA) are proposed. The methods are applied to a numerical model of an aerodynamically excited large composite wind turbine blade with disbonding damage. The GA out performs the other selection methods and enables building multivariate DSFs that markedly enhance early damage detectability and are insensitive to measurement noise. (C) 2015 Elsevier Ltd. All rights reserved.

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