Residual Life Prediction of Rolling Bearing based on AOA-LSTM

Abstract

Aiming at the problem of predicting the remaining life of rolling bearings, a method for predicting the remaining life of bearings based on the arithmetic optimization algorithm (AOA) and the long-short-term memory network (LSTM) fusion algorithm is proposed. First, use the random forest algorithm to analyze the importance of the extracted time-domain and frequency-domain feature indicators, and build a degradation feature decision table; then, use the AOA optimization algorithm to optimize the hyperparameters in the LSTM, and select the optimal hyperparameters to establish predictions model; finally, the degradation features are input into the prediction model for prediction, and the prediction model is evaluated by root mean square error (RMSE) and mean absolute error (MAE). The proposed research method is experimentally verified by the XJTU-SY dataset. The experimental results show that the RMSE and MAE of the proposed research method are 5.56% and 4.37%, respectively. Compared with the MLP model, the RMSE and MAE are reduced by 31.58% and 29.61%, respectively. Compared with the RNN model, the RMSE and MAE are reduced by 24.66% and 25.49%, respectively, which verifies the effectiveness of the research method.