基于分布式强化学习的多泵设备基于状态的维护
Distributional Reinforcement Learning for Condition-Based Maintenance of Multi-Pump Equipment
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中文总结 AI 辅助
本文提出基于分布式强化学习的多泵设备基于状态的维护方法，利用QR-DQN和老化因子，通过三种策略实现高效维护，提升投资回报率和系统稳定性。
中文摘要 AI 辅助
基于状态的维护（CBM）标志着现代工业系统中从被动到主动设备管理策略的转变。传统的时间基于维护计划经常导致不必要的支出和意外设备故障。相反，CBM利用实时设备状态数据来提高维护时机并优化资源分配。本文提出了一种新的分布式强化学习方法，用于多设备CBM，使用带有老化因子的分位数回归深度Q网络（QR-DQN）。本研究的方法涵盖了通过三种战略场景同时管理多个泵单元。实施安全优先、平衡和成本高效的方法是至关重要的。在3000次训练回合的全面实验验证中，所有策略都显示出显著的性能提升。安全优先策略在成本效率方面表现突出，其投资回报率（ROI）为3.91，比替代方案性能提高152%，而投资仅高出31%。系统表现出95.66%的操作稳定性和立即适用于工业环境的能力。
英文摘要
Condition-Based Maintenance (CBM) signifies a paradigm shift from reactive to proactive equipment management strategies in modern industrial systems. Conventional time-based maintenance schedules frequently engender superfluous expenditures and unanticipated equipment failures. In contrast, CBM utilizes real-time equipment condition data to enhance maintenance timing and optimize resource allocation. The present paper proposes a novel distributional reinforcement learning approach for multi-equipment CBM using Quantile Regression Deep Q-Networks (QR-DQN) with aging factor integration. The methodology employed in this study encompasses the concurrent administration of multiple pump units through three strategic scenarios. The implementation of safety-first, balanced, and cost-efficient approaches is imperative. Comprehensive experimental validation over 3,000 training episodes demonstrates significant performance improvements across all strategies. The Safety-First strategy demonstrates superior cost efficiency, with a return on investment (ROI) of 3.91, yielding 152\% better performance than alternatives while requiring only 31\% higher investment. The system exhibits 95.66\% operational stability and immediate applicability to industrial environments.
