An attention-based temporal convolutional network method for predicting remaining useful life of aero-engine

Researches on Remaining Useful Life (RUL) prediction of aero-engine could help to make maintenance plans, improve operation reliabilities and reduce maintenance costs. While deep learning methods have been widely used in RUL prediction research, most deep learning-based RUL prediction methods tend to treat input features as equally important. Contributions of different channels and time steps from input features are not considered simultaneously, which will inevitably affect efficiencies and accuracies of RUL prediction. Therefore, a novel deep learning-based RUL prediction method named attention-based temporal convolutional network (ATCN) is proposed in this article. First, an improved self-attention mechanism is used to weight contributions of different time steps from input features. Input features of time steps closely related to RUL are enhanced by the improved self-attention mechanism, which could improve efficiencies of feature extraction in a network. Then, a temporal convolutional network is constructed to capture long-term dependent information and extract feature representations from weighted features of the improved self-attention mechanism. Next, a squeeze-and-excitation mechanism is adopted to weight contributions of different channels from feature representations, which could help to improve prediction accuracies of the network. Finally, a fully connected layer is constructed to fuse weighted features to output RUL values. A commercial modular aero-propulsion system simulation (C-MAPSS) dataset from NASA is applied to verify effects of the proposed method. Performances of the proposed method are compared with those based on different neural network architectures, such as CNN, RNN, LSTM, DCNN, TCN, BiGRU-TSAM, AGCNN and channel attention plus Transformer. Results show that the proposed method could yield results with higher accuracy for RUL prediction of aero-engine than other methods.