Multi-task spatio-temporal augmented net for industry equipment remaining useful life prediction

Abstract

Accurate estimating the machine health indicator is an essential part of industrial intelligence. Despite having considerable progress, remaining useful life (RUL) prediction based on deep learning still confronts the following two challenges. Firstly, the length of condition monitoring data obtained from sensors is inconsistent, and the existing fixed window data processing method cannot fully adapt to all individual samples. Secondly, it is challenging to extract local and global features for long-series prediction tasks. To address these issues, this paper proposes a Multi-task Spatio-Temporal Augmented Net(MTSTAN) for industrial RUL prediction, which enhances the local features of different sensors data through channel attention mechanism, and proposes a skip connected causal augmented convolution network to enhance the global feature extraction in time series. For the industrial scenario of inconsistent data lengths, a multi-window multi-task sharing mechanism is set up to capture various time dependencies among different time scales. The robustness and universality of the model are increased by sharing information among tasks and multi-task window mechanism. Finally, a large number of experiments were carried out on the turbofan aircraft engine run-to-failure prognostic benchmark dataset (C-MAPSS) to evaluate the proposed model, and compared with the existing 14 state-of-the-art approaches. The results show that the enhancement of local and global time series features can effectively improve the prediction accuracy. The Multi-task learning strategy has excellent applicability in dealing with the problem of inconsistent data length.