文章基本信息

标题：Paris law parameter identification based on the Extended Kalman Filter
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作者：M. Melgar ; M. Melgar ; C. Gomez-Jimenez 等
期刊名称：MATEC Web of Conferences
电子版ISSN：2261-236X
出版年度：2016
卷号：83
页码：1-5
DOI：10.1051/matecconf/20168303003
语种：English
出版社：EDP Sciences
摘要：Aircraft structures are commonly subjected to repeated loading cycles leading to fatigue damage. Fatigue data can be extrapolated by fatigue models which are adopted to describe the fatigue damage behaviour. Such models depend on their parameters for accurate prediction of the fatigue life. Therefore, several methods have been developed for estimating the model parameters for both linear and nonlinear systems. It is useful for a broad class of parameter identification problems when the dynamic model is not known. In this paper, the Paris law is used as fatigue-crack-length growth model on a metallic component under loading cycles. The Extended Kalman Filter (EKF) is proposed as estimation method. Simulated crack length data is used to validate the estimation method. Based on experimental data obtained from fatigue experiment, the crack length and model parameters are estimated. Accurate model parameters allow a more realistic prediction of the fatigue life, consequently, the remaining useful life (RUL) of component can be accurately computed. In this sense, maintenance performance could be improved.