Prediction of Bearing Remaining Service Life Based on CNN-LSTM

Cai Weiwei; Xu Yanwei; Xie Tancheng

doi:10.16578/j.issn.1004.2539.2022.10.003

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Prediction of Bearing Remaining Service Life Based on CNN-LSTM

Theory·Research | 更新时间：2022-11-05

- Prediction of Bearing Remaining Service Life Based on CNN-LSTM
- Journal of Mechanical Transmission Vol. 46, Issue 10, Pages: 17-23(2022)
- 作者机构：
  
  1.河南科技大学机电工程学院，河南洛阳 471003
  2.智能数控装备河南省工程实验室，河南洛阳 471003
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2022.10.003
  CLC：
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蔡薇薇,徐彦伟,颉潭成.基于CNN-LSTM的轴承剩余使用寿命预测[J].机械传动,2022,46(10):17-23.

Cai Weiwei,Xu Yanwei,Xie Tancheng.Prediction of Bearing Remaining Service Life Based on CNN-LSTM[J].Journal of Mechanical Transmission,2022,46(10):17-23.
蔡薇薇,徐彦伟,颉潭成.基于CNN-LSTM的轴承剩余使用寿命预测[J].机械传动,2022,46(10):17-23. DOI： 10.16578/j.issn.1004.2539.2022.10.003.

Cai Weiwei,Xu Yanwei,Xie Tancheng.Prediction of Bearing Remaining Service Life Based on CNN-LSTM[J].Journal of Mechanical Transmission,2022,46(10):17-23. DOI： 10.16578/j.issn.1004.2539.2022.10.003.

摘要

针对轴承到达服役时间而依然满足使用条件造成的资源浪费问题，提出了一种基于CNN-LSTM的轴承剩余使用寿命预测方法。选取已完成服役工作仍健康的高铁牵引电机轴承为研究对象，搭建高铁牵引电机轴承试验平台并采集其振动信号；建立CNN-LSTM的网络模型，将采集到的振动信号经过傅里叶变换后输入到网络模型中，对其深层特征进行挖掘；最后，通过预测模块实现了对剩余使用寿命的预测。结果显示，所提方法得到的预测值较接近真实值，能够很好地反映出轴承运行中的性能退化趋势。

Abstract

Aiming at the waste of resources caused by the bearing reaching the service time and still meeting the service conditions, a bearing remaining service life prediction method based on CNN-LSTM is proposed. Firstly, a high-speed railway traction motor bearing which has completed service but is still healthy is selected as the research object, the test platform is built and the bearing vibration signal is collected; secondly, a network model of CNN-LSTM is established; then, the collected vibration signal is input into the network model after Fourier transform, and its deep features are mined; finally, the remaining service life is predicted through the prediction module. The results show that the predicted value obtained by the proposed method is closer to the true value, which can well reflect the performance degradation trend of the bearing in operation.

关键词

滚动轴承CNN-LSTM剩余使用寿命预测长短时记忆网络

Keywords

Rolling bearingCNN-LSTMRemaining service life predictionLong and short term memory network

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