A Real-Time Remaining Fatigue Life Prediction Approach Based on a Hybrid Deep Learning Network

被引:3
|
作者
Zhu, Yifeng [1 ,2 ]
Zhang, Jianzhao [3 ]
Luo, Jiaxiang [3 ]
Guo, Xinyan [4 ]
Liu, Ziyu [3 ]
Zhang, Ruonan [1 ,2 ]
机构
[1] Taihu Lab Deepsea Technol Sci, Wuxi 214082, Peoples R China
[2] China Ship Sci Res Ctr, Wuxi 214082, Peoples R China
[3] South China Univ Technol, Sch Automation Sci & Engn, Guangzhou 510641, Peoples R China
[4] South China Univ Technol, Sch Civil Engn & Transportat, Guangzhou 510641, Peoples R China
基金
中国国家自然科学基金;
关键词
crack propagation; remaining fatigue life; deep learning; real-time prediction;
D O I
10.3390/pr11113220
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
Fatigue failure is a typical failure mode of welded structures. It is of great engineering significance to predict the remaining fatigue life of structures after a certain period of service. In this paper, a two-stage hybrid deep learning approach is proposed only using the response of structures under fatigue loading to predict the remaining fatigue life. In the first stage, a combination of convolutional neural network (CNN), squeeze-and-excitation (SE) block, and long short-term memory (LSTM) network is employed to calculate health indicator values based on the current measured data sequence. In the second stage, a particle filtering-based algorithm is utilized to predict the remaining fatigue life using the previously calculated health indicators. Experimental results on different welded specimens under the same loading conditions demonstrate that the hybrid deep learning approach achieves superior prediction accuracy and generalization ability compared to CNN, LSTM, or CNN-LSTM models in the first stage. Moreover, the average relative deviation between the predicted and actual fatigue life is less than 6% during the final quarter of the crack propagation and fracture stage.
引用
收藏
页数:16
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