Deep-Convolution-Based LSTM Network for Remaining Useful Life Prediction

Abstract

Accurate prediction of remaining useful life (RUL) has been a critical and challenging problem in the field of prognostics and health management (PHM), which aims to make decisions on which component needs to be replaced when. In this article, a novel deep neural network named convolution-based long short-term memory (CLSTM) network is proposed to predict the RUL of rotating machineries mining the in situ vibration data. Different from previous research that simply connects a convolutional neural network (CNN) to a long short-term memory (LSTM) network serially, the proposed network conducts convolutional operation on both the input-to-state and state-to-state transitions of the LSTM, which contains both time-frequency and temporal information of signals, not only preserving the advantages of LSTM, but also incorporating time-frequency features. The convolutional structure in the LSTM has the ability to capture long-term dependencies and extract features from the time-frequency domain at the same time. By stacking the multiple CLSTM layer-by-layer and forming an encoding-forecasting architecture, the deep learning model is established for RUL prediction in this article. Run-to-failure tests on bearings are conducted, and vibration responses are collected. Using the proposed algorithm, RUL is predicted, and as a comparison, the performance from other methods, including deep CNNs and deep LSTM, is evaluated using the same dataset. The comparative study indicates that the proposed CLSTM network outperforms the current deep learning algorithms in URL prediction and system prognosis with respect to better accuracy and computation efficiency.