Temperature is an important factor affecting the normal operation of sliding bearings.Real-time monitoring of bearing temperature is crucial for testing,but the bearing temperature data collected in real time is large.In order to solve the problem of data loss caused by temperature sensor fault during testing or monitoring process,a long short-term memory (LSTM) network prediction model is proposed and compared with traditional BP neural network prediction model.The temperature trend at subsequent moments is predicted by processing the existing temperature data,and the root mean square error (RMAE) of two models is compared to describe the prediction accuracy.The mutual validation between actual values and predicted values verifies that the LSTM network prediction model has good prediction accuracy with RMSE as 0.0805,compensating for shortcomings of BP neural network prediction model accuracy effectively and solving the problem of sample dependence in BP neural network.
关键词
滑动轴承/油膜轴承/神经网络/时间序列/温度/预测
Key words
sliding bearing/oil film bearing/neural network/time series/temperature/prediction
维普
万方数据