Dual Channel Feature Attention-Based Approach for RUL Prediction Considering the Spatiotemporal Difference of Multisensor Data
Hui Gao, Yibin Li, Ying Zhao, Yan Song
Abstract
The remaining useful life (RUL) prediction has always been the key technology to realize predictive maintenance. An accurate prediction can give decision-makers a reliable reference to develop maintenance schedules and adjust production planning. When dealing with the spatiotemporal data of multisensor system, recent deep learning (DL) methods, however, still remain unexplored to weigh the contributions from both spatial and temporal dimensions. In this article, we propose a novel DL-based approach with dual channel feature attention (DCFA) modules. First, the two-individual feature attention branches are used to automatically weigh the input on both time and spatial domain, which helps the model to focus more attention on the important elements. Then multilayer bidirectional long short-term memory (Bi-LSTM) and convolutional neural networks are used to extract the high-level features. Finally, a fusion network will combine the features to estimate the RUL. Evaluation experiments are conducted on the C-MAPSS dataset to verify the performance of the proposed model. The results show that the proposed model outperforms other state-of-the-art approaches.