An attention-based multi-scale temporal convolutional network for remaining useful life prediction

Remaining Useful Life (RUL) prediction is one of the key technologies in Prognostics and Health Management (PHM). Accurate RUL prediction is crucial for equipment reliability, and equipment sensor data contains information at different scales that can be used for RUL prediction. Current mainstream methods usually extract features from only a single scale, neglecting the information from other scales. Additionally, the attention mechanisms used in these methods do not incorporate multi-dimensional information, which can lead to a loss in prediction performance. To address this issue, this paper proposes an end-to-end framework which utilizes Multi-Scale Temporal Convolutional Network (MSTCN) to extract multi-scale features and introduces Self-Attention (SA) and Global Fusion Attention (GFA) mechanisms to assist MSTCN's work. SA is located at the head of MSTCN, emphasizing the features of those steps that are particularly important, thereby improving the extraction efficiency of MSTCN. GFA is an original design of attention mechanism located at the end of MSTCN that intelligently integrates multi-dimensional attention information, suppressing redundant information in the multi-scale features. To validate the effectiveness of the proposed method, we conduct experiments on the C-MAPSS dataset released by NASA. The results show that our method outperforms other state-of-the-art prediction methods in terms of RUL prediction performance.