Prediction on the Remaining Useful Life of Rolling Bearings Using Ensemble DLSTM

As a significant component of rotation machinery, bearing plays a role in supporting and transmitting power. However, bearings are subject to complex operating conditions and are prone to failure. To avoid ineffectiveness and improve the reliability of bearings, a data-driven method is used to predict the remaining useful life (RUL). However, this method is less stable and can only forecast the RUL of bearings under training sample conditions. An ensemble deep, long-term, and short-term memory (EDLSTM) method is proposed to solve this problem. First, the feature of the forecast-bearing RUL was extracted including time-domain features, frequency-domain energy features, and Shannon entropy. Then, a deep long- and short-term memory network prediction model of the bearing RUL was constructed. To resolve the instability of DLSTM predictions, multiple DLSTMs were ensembled using the maximum information component (MIC) criterion. The model i trained using bearing data with different failure modes under difficult operating conditions to improve the predictive stability of the model. Finally, an EDLSTM was constructed to achieve the bearing RUL prediction. In the prediction result of the training set, the cumulative relative accuracy (CRA) was above 0.9 for most of the bearings. According to the experimental results in the test set, the mean CRA was over 0.80. For some of the bearing's RUL, the CRA was more than 0.90. The above results show that the proposed approach can effectively predict the RUL of a bearing and has a more stable prediction ability than the bagging integration method.