Prediction of bearing remaining useful life based on DACN-ConvLSTM model

The accurate prediction for the remaining useful life (RUL) of bearing is essential for ensuring the reliable operation and establishing an effective maintenance strategy. During the whole life of bearing, the vibration signal contains more and more nonlinear information of performance degradations. A novel model named dynamically activated convolutional network (DACN) is proposed to extract the nonlinear information of vibration signal. DACN can execute the feature extraction on the input and adaptively activate the features. Based on DACN, DACN-ConvLSTM is proposed for the RUL prediction of rolling bearing. In model, ConvLSTM can mine the timing information between adjacent signal samples. which improves the prediction accuracy of bearings. Finally, the model is verified by two cases. The features are visualized by using t-SNE method which provides effective prediction pictures. The experiment results present the proposed DACN-ConvLSTM method possesses the higher RUL prediction accuracy compared with other methods, and can maintain the prediction performance on different datasets.