An online data-driven method for predicting crack propagation and remaining fatigue life via combining linear and nonlinear ultrasonic.

Journal: Ultrasonics
Published Date: May 26, 2025

Abstract

Ultrasonic features play a critical role in evaluating the structural integrity of metallic components, yet current approaches predominantly rely on individual ultrasonic parameters for predictive analysis. This study presents an online data-driven method that combines linear and nonlinear ultrasonic parameters through an optimized weighting function to predict crack propagation and remaining fatigue life (RFL) over the entire fatigue life from microstructural changes to macroscopic crack formation of plate structures. LSTM neural networks are employed to learn sequential features captured by various PZTs. Experimental results on 6061 aluminum plates demonstrate that the proposed method predicts crack length and RFL with average errors of 0.568 mm and 4.50 % of the total fatigue life of the structure, respectively. Comparative analysis reveals that the combined approach with the optimal weighting function outperforms predictions using individual parameters. This method shows significant robustness under varying conditions, underscoring its potential for real-time fatigue monitoring and predictive maintenance.

Authors

  • Jiachen Zhou
    Shanghai Key Laboratory of Intelligent Sensing and Detection Technology, School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai 200237, China.
  • Lishuai Liu
    Shanghai Key Laboratory of Intelligent Sensing and Detection Technology, School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai 200237, China; Shanghai Institute of Aircraft Mechanics and Control, Shanghai 200092, China. Electronic address: lishuai.liu@ecust.edu.cn.
  • Haiming Xu
    Shanghai Key Laboratory of Intelligent Sensing and Detection Technology, School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai 200237, China.
  • Yanxun Xiang
    Shanghai Key Laboratory of Intelligent Sensing and Detection Technology, School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai 200237, China. Electronic address: yxxiang@ecust.edu.cn.
  • Fu-Zhen Xuan
    Shanghai Key Laboratory of Intelligent Sensing and Detection Technology, School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai 200237, China.

Keywords

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