期刊
DATA & KNOWLEDGE ENGINEERING
卷 149, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.datak.2023.102240
关键词
Deep reinforcement learning; Probabilistic modeling; Input-output hidden Markov model; Predictive maintenance; Industry 5.0; Interpretable reinforcement learning
Deep reinforcement learning has significant potential in industrial decision-making, but its lack of interpretability poses challenges for safety-critical systems. This paper introduces a novel approach that combines probabilistic modeling and reinforcement learning, addressing these challenges and achieving excellent results in predictive maintenance for turbofan engines.
Deep reinforcement learning holds significant potential for application in industrial decision-making, offering a promising alternative to traditional physical models. However, its black-box learning approach presents challenges for real-world and safety-critical systems, as it lacks interpretability and explanations for the derived actions. Moreover, a key research question in deep reinforcement learning is how to focus policy learning on critical decisions within sparse domains. This paper introduces a novel approach that combines probabilistic modeling and reinforcement learning, providing interpretability and addressing these challenges in the context of safety-critical predictive maintenance. The methodology is activated in specific situations identified through the input-output hidden Markov model, such as critical conditions or near-failure scenarios. To mitigate the challenges associated with deep reinforcement learning in safety-critical predictive maintenance, the approach is initialized with a baseline policy using behavioral cloning, requiring minimal interactions with the environment. The effectiveness of this framework is demonstrated through a case study on predictive maintenance for turbofan engines, outperforming previous approaches and baselines, while also providing the added benefit of interpretability. Importantly, while the framework is applied to a specific use case, this paper aims to present a general methodology that can be applied to diverse predictive maintenance applications.
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