Remaining useful life prediction based on double self-attention mechanism and long short-term memory network

Prediction of remaining useful life (RUL) is an important part of fault prognostic and health management. Traditional long short-term memory (LSTM) network cannot select the key features actively, and it is difficult to effectively extract the degradation information contained in big data. This paper proposes an RUL prediction approach based on an improved LSTM network, where the random forest (RF) algorithm is adopted to filter the input features in order to select key features actively. A double self-attention mechanism is used to complete the adaptive weight assignment from feature dimension and the time dimension. Thus, the proposed approach can focus on the key features and historical time during the learning process. By fusing the statistical features, the model can improve the accuracy of RUL prediction. To illustrate the effectiveness of the proposed method, a case study is conducted with a data set of aircraft engine. The results indicate that the proposed method can effectively improve the accuracy of RUL prediction with complicated data sets. © 2024 Chinese Institute of Electronics. All rights reserved.