Open Access

ARTICLE

Aluminum Alloy Fatigue Crack Damage Prediction Based on Lamb Wave-Systematic Resampling Particle Filter Method

Gaozheng Zhao¹, Changchao Liu¹, Lingyu Sun¹, Ning Yang², Lei Zhang¹, Mingshun Jiang¹, Lei Jia¹, Qingmei Sui^1,*

1 College of Control Science and Engineering, Shandong University, Jinan, 250061, China
2 Shandong Institute of Space Electronic Technology, Yantai, 264010, China

* Corresponding Author: Qingmei Sui. Email:

Structural Durability & Health Monitoring 2022, 16(1), 81-96. https://doi.org/10.32604/sdhm.2022.016905

Received 08 April 2021; Accepted 21 July 2021; Issue published 11 February 2022

Abstract

Fatigue crack prediction is a critical aspect of prognostics and health management research. The particle filter algorithm based on Lamb wave is a potential tool to solve the nonlinear and non-Gaussian problems on fatigue growth, and it is widely used to predict the state of fatigue crack. This paper proposes a method of lamb wave-based early fatigue microcrack prediction with the aid of particle filters. With this method, which the changes in signal characteristics under different fatigue crack lengths are analyzed, and the state- and observation-equations of crack extension are established. Furthermore, an experiment is conducted to verify the feasibility of the proposed method. The Root Mean Square Error (RMSE) of the three different resampling methods are compared. The results show the system resampling method has the highest prediction accuracy. Furthermore, the factors affected by the accuracy of the prediction are discussed.

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Keywords

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