Using temporal convolution network for remaining useful lifetime prediction

Abstract

Because Convolutional Neural Network (CNN) can extract spatial feature, while Long Short-Term Memory (LSTM) can learn temporal features, many methods combining CNN with LSTM are proposed for remaining useful lifetime prediction. In practice, it is better to learn temporal features from long history sequence data because of the slow inherently long-term degradation process. However, LSTM is less efficient in processing long history sequence. To solve this problem, in this work, a temporal convolution network combining causal filters with dilated convolutions is used to expand the receptive field length of network. The network structure can be fixed through three key parameters, and the size of time window adopted for time sequence processing is the same as the receptive field length. These two characteristics allow the network to easily be applied for engineering purposes. The method is tested and evaluated using two well-known datasets, namely the “Turbofan Engine Degradation Simulation Dataset C-MAPSS” and “Milling Dataset.” The performance analysis shows that the proposed method outperforms more classical methods in terms of prediction accuracy.