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A review of reinforcement learning-based intelligent maintenance decision-making for equipment
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Xinyi WAN, Chuanyang LI, Changhua HU, Zeming ZHANG, Mingzhe LENG
Journal of Vibration Engineering | 2025, 38(6) : 1154 - 1166
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Journal of Vibration Engineering | 2025, 38(6): 1154-1166
A review of reinforcement learning-based intelligent maintenance decision-making for equipment
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Xinyi WAN, Chuanyang LI, Changhua HU, Zeming ZHANG, Mingzhe LENG
Affiliations
  • Laboratory of Intelligent Control,PLA Rocket Force University of Engineering,Xi’an 710025,China
Published: 2025-06-10 doi: 10.16385/j.cnki.issn.1004-4523.2025.06.004
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The increasing complexity of intelligent equipment and evolving operation and maintenance demands within Industry 4.0 highlight the inadequate adaptability of traditional maintenance decision-making methods in dynamic environments. Reinforcement learning (RL)-based maintence decision-making technology offers a paradigm for intelligent equipment maintenance by enabling autonomous strategy optimization through environmental interaction. This paper systematically explores the integration of RL theory and maintenance decision-making, focuses on 76 peer-reviewed articles published between 1954 and 2024. Core RL algorithms, including SARSA, Q-Learning, and Actor-Critic, are thoroughly examined and analyzed. The current state of intelligent equipment maintenance decision-making technology is also analyzed in depth. Typical application scenarios for RL in equipment maintenance decision-making are comprehensively dissected across four key areas: industrial manufacturing, energy, aerospace, and transportation. The study also identifies and discusses the core challenges facing current technology, such as algorithm convergence speed, computational efficiency, model interpretability, and issues related to data acquisition and privacy. This research provides a theoretical reference for algorithm innovation and engineering implementation in the field of intelligent operation and maintenance, fostering the deeper application of RL in maintenance decision-making.

fault diagnosis  /  reinforcement learning  /  intelligent maintenance decision  /  artificial intelligence
Xinyi WAN, Chuanyang LI, Changhua HU, Zeming ZHANG, Mingzhe LENG. A review of reinforcement learning-based intelligent maintenance decision-making for equipment[J]. Journal of Vibration Engineering, 2025 , 38 (6) : 1154 -1166 . DOI: 10.16385/j.cnki.issn.1004-4523.2025.06.004
Year 2025 volume 38 Issue 6
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doi: 10.16385/j.cnki.issn.1004-4523.2025.06.004
  • Receive Date:2025-05-15
  • Online Date:2026-02-12
  • Published:2025-06-10
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  • Received:2025-05-15
  • Revised:2025-05-26
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    Laboratory of Intelligent Control,PLA Rocket Force University of Engineering,Xi’an 710025,China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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