Pseudo-Label-Vector-Guided Parallel Attention Network for Remaining Useful Life Prediction

Abstract

Prognostic health management (PHM) has become important in many industries as a critical technology to increase machine stability and operational efficiency. Recently, various methods using deep learning to estimate the remaining useful life (RUL) as a core task of PHM have been proposed. However, the existing attention methods do not explicitly capture the correlation between temporal and spatial time series, reducing the RUL prediction accuracy. This article proposes a novel RUL prediction algorithm using a spatiotemporal attention mechanism based on the pseudo-label vectors to solve this problem. The proposed attention network uses the pseudo-label vector learned in the intermediate prediction process as a query vector to focus on time sequence data related to the RUL. Therefore, compared with conventional attention models that extract correlations for all the sequences, the proposed model captures features directly related to RUL with less computational cost. Experiments have been performed on two widely used datasets, and the experimental results show that the proposed approach outperforms the state of the art for root-mean-square error, with averages 4.27 and 3039 in the NASA Commercial Modular Aero-Propulsion System Simulation dataset and the IEEE PHM 2012 Prognostic challenge dataset, respectively. In addition, the analysis in the experiment reveals that the proposed model has better interpretability than the existing models by obtaining the correlation between time-series data and the RUL through the attention score in terms of time and features.