DT-PPO : a real-time multisensor-driven predictive maintenance framework

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Zhou, Ke and Zhong, Xiang and Shao, Haidong and Zhang, Haomiao and Liu, Bin (2025) DT-PPO : a real-time multisensor-driven predictive maintenance framework. Reliability Engineering and System Safety. ISSN 0951-8320 (In Press)

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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.

ORCID iDs

Zhou, Ke, Zhong, Xiang, Shao, Haidong, Zhang, Haomiao and Liu, Bin ORCID logoORCID: https://orcid.org/0000-0002-3946-8124;
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