文献类型：期刊文献

英文题名：Data-driven online prediction of remaining fatigue life of a steel plate based on nonlinear ultrasonic monitoring

作者：Sun, Di[1];Zhu, Wujun[1,2];Xiang, Yanxun[1];Xuan, Fu-Zhen[1]

机构：[1]East China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai Key Lab Intelligent Sensing & Detect Tech, Shanghai 200237, Peoples R China;[2]Nanjing Univ, Key Lab Modern Acoust, MOE, Nanjing 210023, Peoples R China

年份：2024

卷号：142

外文期刊名：ULTRASONICS

收录：;EI(收录号：20242316196969);WOS:【SCI-EXPANDED(收录号:WOS:001251829400001)】；

基金：This work was supported by the National Key Research and Devel- opment Program of China (2021YFC3001802) , the Fundamental Research Funds for the Central Universities (Grants No. 020414380195) and the National Natural Science Foundation of China (Grants No. 12025403, 12327807, 52321002, 12004114) .

语种：英文

外文关键词：Ultrasonic nonlinearity; Remaining fatigue life prediction; Online monitoring; Fatigue damage

摘要：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 decisionmaking.