Multiscale Convolutional Attention Network for Predicting Remaining Useful Life of Machinery

To integrate the complete degradation information of machinery, deep learning-based prognostics approaches usually use monitoring data acquired by different sensors as the inputs of networks. These approaches, however, lack an explicit learning mechanism to effectively identify the distinctions of different sensor data and highlight the important degradation information, thereby affecting the accuracy of deep prognostics networks and limiting their generalization. To overcome the aforementioned weaknesses, a new deep prognostics framework named multiscale convolutional attention network (MSCAN) is proposed in this article for predicting the remaining useful life (RUL) of machinery. In the proposed MSCAN, self-attention modules are first constructed to effectively fuse the input multisensor data. Then, a multiscale learning strategy is developed to automatically learn representations from different temporal scales. Finally, the learned high-level representations are fed into dynamic dense layers to perform regression analysis and RUL estimation. The proposed MSCAN is evaluated using multisensor monitoring data from life testing of milling cutters, and also compared with some state-of-the-art prognostics approaches. Experimental results demonstrate the effectiveness and superiority of the proposed MSCAN in fusing multisensor information and improving RUL prediction accuracy.