Performance degradation prediction model of rolling bearing based on self-checking long short-term memory network

Abstract

The prediction of performance degradation is of great significance for the health monitoring of rolling bearings. When predicting the performance degradation trend for the whole-life data of bearings, there are problems such as long prediction time, high cost and single evaluation criteria for prediction effect, which hinder the accuracy of degradation prediction. To this end, a self-checking long and short-term memory (Sc-LSTM) prediction model is proposed in this paper for predicting the performance degradation trend of bearings. First, Relation was used as a performance degradation indicator and improved using the mean and normalization methods. This approach can effectively alleviate the problems of long forecasting time and high cost. Secondly, the long and short-term memory (LSTM) sets up many different prediction schemes and proposes a test layer by segmental prediction and comparison to select the appropriate test index σ. According to the robust stability of the self-checking method and the high accuracy of LSTM nonlinear prediction, an Sc-LSTM performance degradation prediction model is established. The model introduces the prediction effect assessment of prediction pass rate η to make up for the problem of inadequate assessment of prediction effect by a single error. Finally, using the Sc-LSTM model, relation is used to perform segmental predictive analysis and experimental validation of the overall prediction. Experiments show that the model can effectively improve the prediction accuracy and is feasible in the prediction of bearing performance degradation.