华东交通大学学报2025，Vol.42Issue(4)：48-60,13.
基于深度学习的列车制动盘剩余使用寿命预测研究
Research on Prediction of Remaining Useful Life of Train Brake Disc Based on Deep Learning
摘要
Abstract
To achieve accurate prediction of the remaining useful life(RUL)of brake discs,ensure train braking safety,and optimize economical maintenance,this paper proposes a prediction model based on the fusion of self-attention mechanism and long short-term memory network(BiLSTM-SA),which takes the crack propagation life as the division basis.Firstly,the test data of brake discs are collected and the working conditions are calibrat-ed,and a thermal-mechanical coupling finite element model is established to obtain the simulation dataset.Sec-ondly,a Time-GAN neural network is constructed,which enhances data through a double-layer LSTM generator and a physical constraint discriminator.Its distribution similarity,root mean square error and coefficient of deter-mination are significantly better than traditional models.Finally,the BiLSTM-SA fusion prediction model is pro-posed,which uses bidirectional LSTM and self-attention mechanism to capture temporal dependencies and key features.In the prediction of single expanding cracks,the RMSE is reduced by 49.8%and 46.5%compared with the traditional LSTM and TCN-LSTM,respectively.In complex working conditions,the RMSE and Score are optimized by 25.5%and 51.1%,respectively,significantly improving the prediction accuracy and robustness.This study can provide a reliable technical solution for condition monitoring and preventive maintenance of high-speed train brake discs.关键词
制动盘/疲劳裂纹/剩余寿命预测/时间序列生成对抗网络/自注意力机制Key words
brake disc/fatigue crack/remaining life prediction/time series generation adversarial network/self-attention mechanism分类
交通工程引用本文复制引用
朱海燕,许晋华,徐晨钊,李祥坤,周生通..基于深度学习的列车制动盘剩余使用寿命预测研究[J].华东交通大学学报,2025,42(4):48-60,13.基金项目
国家自然科学基金项目(52162045) （52162045）
江西省自然科学基金重点项目(20232ACB204022) （20232ACB204022）
轨道交通运载系统全国重点实验室开放课题(RVL2403) （RVL2403）
江西省2023年度研究生创新专项资金项目(YC2023-S464) （YC2023-S464）
