轻工机械2024，Vol.42Issue(3)：66-73,79,9.DOI:10.3969/j.issn.1005-2895.2024.03.010
基于Bi-TCN-LSTM的滚动轴承剩余使用寿命预测方法
Prediction Method for Remaining Useful Life of Rolling Bearings Based on Bidirectional Temporal Convolutional Network and Long Short-Term Memory Network
摘要
Abstract
Due to the insufficient sensing field of the temporal convolutional networks(TCN),the key degradation information of the bearing is often ignored,which results in poor prediction of the remaitning useful life(RUL)of bearings.Moreover,the long-term dependence problem of long short-term memory(LSTM)may not be well solved with the increase of data volume and sequence length.Therefore a new prediction method based on Bidirectional temporal convolutional network andLong short-term memory(Bi-TCN-LSTM)was proposed.Firstly,the multi-sensor data was normalized and fused,and then the Bi-TCN-LSTM was used for data feature extraction and deep learning,in which the convolutional attention mechanism(CAM)was introduced into the TCN module,and the three gates of the LSTM were simplified into one gate.It effectively accelerated the learning speed of the prediction model and improved the accuracy of the prediction model.The IEEE PHM 2012 bearing dataset was used to carry out the RUL prediction experiments.The results show that compared with other advanced prediction models,the Bi-TCN-LSTM method has relatively lower prediction error and better performance.关键词
滚动轴承/剩余使用寿命预测/多传感器融合/时间卷积网络/长短期记忆网络Key words
rolling bearing/RUL(Remaining Useful Life)prediction/MSF(Multi-Sensor Fusion)/TCN(Temporal Convolutional Networks)/LSTM(Long Short-Term Memory Networks)分类
机械制造引用本文复制引用
高萌,鲁玉军..基于Bi-TCN-LSTM的滚动轴承剩余使用寿命预测方法[J].轻工机械,2024,42(3):66-73,79,9.基金项目
浙江省重点研发项目(2022C01242) （2022C01242）
浙江理工大学龙港研究院项目(LGYTY2021004). （LGYTY2021004）
