| Issue |
MATEC Web Conf.
Volume 83, 2016
CSNDD 2016 - International Conference on Structural Nonlinear Dynamics and Diagnosis
|
|
|---|---|---|
| Article Number | 03003 | |
| Number of page(s) | 5 | |
| Section | Mechanical, aeronautic, aerospace and naval structures | |
| DOI | https://doi.org/10.1051/matecconf/20168303003 | |
| Published online | 16 November 2016 | |
Paris law parameter identification based on the Extended Kalman Filter
1 Institut Clment Ader ; Universit de Toulouse ; INSA, 3, rue Caroline Aigle, 31400, Toulouse, France
2 INSA ; Universit de Toulouse, 135, avenue de rangueil, 31077, Toulouse, France
3 Institut de Mathmatiques de Toulouse ; Universit de Toulouse ; UPS, 118, route de Narbonne, 31062, Toulouse, France
4 LAPLACE INP ; Universit de Toulouse ; UPS, 118, route de Narbonne, 31062, Toulouse, France
a e-mail: lea.cot@insa-toulouse.fr
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.
© The Authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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