Prediction of the remaining useful life of rolling bearings by LSTM based on multidomain characteristics and a dual-attention mechanism

This study proposes a framework for bearing remaining useful life (RUL) prediction that uses multidomain features and a dual-attention mechanism (DAM). First, sparsity measures are introduced as new feature parameters to comprehensively and accurately extract the degradation features of bearings. Second, a long short-term memory network integrated with DAM is applied for RUL prediction. DAM simultaneously applies the attention mechanism to the time steps and feature dimension to increase the attention to important information and enhance the prediction performance of the network. Third, a pseudo-normalization method is proposed to solve the problem of unknown bearing test data in actual working conditions under the premise of retaining the original data characteristics and RUL prediction accuracy as much as possible. Lastly, the proposed framework is experimentally proven on public datasets and compared with other methods to prove its feasibility and effectiveness.