Data-driven predictive maintenance framework considering the multi-source information fusion and uncertainty in remaining useful life prediction

Abstract

The existing equipment maintenance methods mainly separate the two related phases of prediction and predictive maintenance (PdM) by looking at remaining useful life (RUL) prediction without considering maintenance or optimizing maintenance schedules based on the given prediction information. To address this issue, we propose a framework based on a multi-stream attention fusion network with quantile regression model and deep reinforcement learning (DRL). In this novel framework, the multi-stream attention fusion block is used to comprehensively capture the operating status of industrial equipment and eliminate the extracted duplicate information. Quantile regression is employed to obtain the probabilistic RUL prediction results with uncertainty expression. We further formulate the PdM problem for DRL, where maintenance actions are triggered based on the estimates of the RUL distribution. To illustrate the superiority of our framework, we compare it with some state-of-the-art models using a public data and our private data. The experimental results indicate that our method exhibits high accuracy and stability in bearing RUL prediction and predictive replacement.