DT-PPO: A Real-Time multisensor-driven predictive maintenance framework

Ke Zhou, Xiang Zhong, Haidong Shao, Haomiao Zhang and Bin Liu

Reliability Engineering and System Safety, 2025, vol. 262, issue C

Abstract: Prognostics and health management (PHM) encompasses both predictive maintenance and health monitoring efforts. However, most studies either focus on prognostics or decision-making in isolation, with only a few attempts to integrate residual life prediction with maintenance strategies. Yet the resulting solutions are often cumbersome and impractical for real-world application. To address this gap, this paper proposes a dynamic transition-proximal policy optimization (DT-PPO) network for predictive maintenance of mechanical equipment. The framework processes multisensory data containing degradation information at different scales, and accurately transforms the initial equipment states into belief states, enabling real-time maintenance decisions. First, a state transition network (TransStateNet) is built as a deep self-encoder to selectively extract multisensory features at different scales and output belief states. Second, a DT-PPO architecture based on belief states is designed to develop maintenance strategies, including spare parts ordering and downtime planning. Finally, an s-g policy is incorporated into the DT-PPO to guide action selection, while state sliding windows process multi-sensor sequence data to reduce uncertainty in the action space, enhancing the robustness of the maintenance strategy. Experimental comparisons against benchmark strategies validate the superiority of the proposed framework.

Keywords: Dynamic transiting; Real-time decision making; Multi-sensor fusion; Proximal strategy optimization; Predictive maintenance (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:262:y:2025:i:c:s0951832025004284

DOI: 10.1016/j.ress.2025.111227

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