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Data-driven online prediction of remaining fatigue life of a steel plate based on nonlinear ultrasonic monitoring.
Sun, Di; Zhu, Wujun; Xiang, Yanxun; Xuan, Fu-Zhen.
Afiliação
  • Sun D; 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.
  • Zhu W; 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; Key Laboratory of Modern Acoustics, MOE, Nanjing University. Nanjing 210023, China. Electronic address: wujunz
  • Xiang Y; 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.
  • Xuan FZ; 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.
Ultrasonics ; 142: 107356, 2024 Aug.
Article em En | MEDLINE | ID: mdl-38833816
ABSTRACT
Online monitoring fatigue damage and remaining fatigue life (RFL) prediction of engineering structures are essential to ensure safety and reliability. A data-driven online prediction method based on nonlinear ultrasonic monitoring was developed to predict the RFL of the structures in real-time. Nonlinear ultrasonic parameters were obtained to monitoring the fatigue degradation. A Bayesian framework was employed to continuously compute and update the RFL distributions of the structures. Nonlinear ultrasonic experiments were performed on the fatigue damaged Q460 steel to validate the developed prediction methodology. The result indicates that the developed method has high prediction accuracy and can provide effective information for subsequent decision-making.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Ultrasonics Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Ultrasonics Ano de publicação: 2024 Tipo de documento: Article