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DT-PPO: A Real-Time multisensor-driven predictive maintenance framework
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AI
DOI：10.1016/j.ress.2025.111227.png)
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Abstract
En 中文
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
Journal
R
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11
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9.0K
Citations:
4.2W