Residual Convolution Long Short-Term Memory Network for Machines Remaining Useful Life Prediction and Uncertainty Quantification

Abstract

Recently, deep learning (DL) has been widely used in the field of remaining useful life (RUL) prediction. Among various DL technologies, recurrent neural network (RNN) and its variant, e.g., long short-term memory (LSTM) network, have gained extensive attention for their ability to capture temporal dependence. Although existing RNN-based methods have demonstrated their RUL prediction effectiveness, they still suffer from the following two limitations: 1) it is difficult for the RNN to directly extract degradation features from original monitoring data and 2) most RNN-based prognostics methods are unable to quantify RUL uncertainty. To address the aforementioned limitations, this paper proposes a new prognostics method named residual convolution LSTM (RC-LSTM) network. In the RC-LSTM, a new ResNet-based convolution LSTM (Res-ConvLSTM) layer is stacked with a convolution LSTM (ConvLSTM) layer to extract degradation representations from monitoring data. Then, under the assumption that the RUL follows a normal distribution, an appropriate output layer is constructed to quantify the uncertainty of prediction results. Finally, the effectiveness and superiority of the RC-LSTM are verified using monitoring data from accelerated bearing degradation tests.