Lifetime prediction of bearings based on the Transformer-LSTM network
ZHANG Fan1,2， YAO Dechen1,2， YAO Shengzhuo1， YANG Jianwei1,2， WANG Yanliang1,2， WEI Minghui1,2， HU Zhongshuo3
1.School of Machine-Electricity and Automobile Engineering, Beijing University of Civil Engineering Architecture，Beijing 100044, China;
2.Beijing Key Laboratory of Performance Guarantee on Urban Rail Transit Vehicles, Beijing University of Civil Engineering Architecture, Beijing 100044, China；
3.School of Mechanical and Electronic Control Engineering, Beijing Jiaotong University, Beijing 100044, China
Abstract：Bearing is an important component in rotating machinery. Due to the working conditions, materials, and processing methods, the lifetimes of bearing have large fluctuations. Traditional parallel or serial neural network prediction methods have seriously dependent on the data sets. Therefore, there is a need for bearings RUL prediction network that can be applied to different data lengths. To overcome this challenge, a Transformer-LSTM serial-parallel neural network prediction model is proposed, which can predict the RUL for bearings with different lifetimes. By reconfiguring the Transformer decoding layer and fusing it with the LSTM network structure, the serial-parallel prediction processing of bearing life data is achieved. The experimental results show that the Transformer-LSTM neural network can accurately predict the bearing failure time for different lifetimes, including: long, medium, and short. Moreover, the model has a stronger generalization ability which also indicates that the proposed method can improve the prediction accuracy of bearing life.
张帆1,2，姚德臣1,2，姚圣卓1，杨建伟1，2，王琰亮1，2，魏明辉1，2，胡忠硕3. 基于Transformer-LSTM网络的轴承寿命预测[J]. 振动与冲击, 2024, 43(6): 320-328.
ZHANG Fan1,2， YAO Dechen1,2， YAO Shengzhuo1， YANG Jianwei1,2， WANG Yanliang1,2， WEI Minghui1,2， HU Zhongshuo3. Lifetime prediction of bearings based on the Transformer-LSTM network. JOURNAL OF VIBRATION AND SHOCK, 2024, 43(6): 320-328.
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