Research on remaining useful life of rolling bearings using EWT-DI-ALSTM

Abstract

The bearing is one of the most essential parts of rotating machinery and equipment, and the accurate prediction of the remaining useful life (RUL) of the bearing is of great engineering significance. This paper proposes a novel hybrid prediction architecture called the long short-term memory (LSTM) based on an attention mechanism combined with empirical wavelet transform (EWT) to improve the prediction accuracy of the RUL of bearings. The prediction architecture consists of three parts: firstly, the vibration signal of the bearing is stationarily processed into several intrinsic mode functions (IMFs) by EWT decomposition. Then, a novel IMF degradation index based on weighted energy entropy and the variance contribution rate is proposed to select the representative IMFs with more degradation characteristics of the bearing. Finally, the selected IMFs are regarded as inputs for the network model, and the LSTM network model based on the attention mechanism is used to track the degradation state of the bearing and accurately used to predict its remaining life. The effectiveness of the proposed method is demonstrated by the experimental data.