Bearing remaining useful life prediction with convolutional long short-term memory fusion networks

Deep learning methods have improved the performance of RUL prediction, and multi-sensor data has also been found can significantly improve the fault diagnosis's accuracy. Hence, it is also highly motivated to integrate the deeply learned features from multi-sensor data for RUL prediction. In this paper, a novel deep learning framework with multi-branch networks, which is called convolutional long short-term memory fusion networks (CLSTMF), is proposed for RUL prediction with multi-sensor data. In each branch networks, shallow features of single sensor's data are extracted by convolutional layer of convolutional neural network (CNN), and then convolutional long short-term memory (CLSTM) network is employed to capture deep temporal features from these shallow features. Meanwhile, a novel information transfer layer (ITL) is developed to fuse the multi-sensor data's features captured with CLSTM in different branch networks. Experiments are also performed on two real run-to-failure datasets and the results indicates that the proposed approach performs well with respect to higher accuracy.