Cascade Fusion Convolutional Long-Short Time Memory Network for Remaining Useful Life Prediction of Rolling Bearing

Deep learning has been a widely adopted approach to achieve the remaining useful life prediction (RUL) of rolling bearing. However, the architectures of the current proposed deep learning approaches are limited and the prediction result is less stable on account of the single sensory data adopted. To address this issue, a new cascade fusion cascade convolutional long-short time memory network is proposed for bearing RUL prediction, in which a cross connection block is formulated to fuse the information streams from the adjacent channels twice and a concentration operation is also affiliated in the end of the network to integrate the separated information streams into an ensemble form. Meanwhile, a convolutional long-short time memory network is adopted as the basic cell in the proposed network on account of its ability to reflect the spatial-temporal correlation of the representative features. Moreover, a smoothing method based on multi-averaging operation is constructed in the prediction phase to largely eliminate the fluctuation in the prediction results. The application on the experimental bearing degradation dataset is able to verify the superiority and stability of the proposed method in comparison with the other comparison methods.