用分布强化学习优化多泵设备状态维修，更省成本更稳定。
Distributional Reinforcement Learning for Condition-Based Maintenance of Multi-Pump Equipment
- 引入带老化因子的分位数回归DQN，同时管理多泵
- 安全优先策略投资回报率达3.91，性能提升152%
- 系统稳定性95.66%，可直接用于工业场景
基于状态的维护（CBM）标志着现代工业系统中从被动维修向主动管理的范式转变。传统的定时维护常导致不必要的支出和意外故障。相比之下，CBM利用实时设备状态数据优化维护时机并合理分配资源。本文提出一种新型分布强化学习方法，采用带老化因子的分位数回归深度Q网络（QR-DQN）实现多泵设备的CBM。研究涵盖三种协同管理策略：安全优先、平衡与成本效益。在3000个训练回合的全面实验验证中，各策略均表现显著提升。安全优先策略展现出最优成本效益，投资回报率（ROI）达3.91，性能较其他方案提升152%，且仅需31%更高投入。系统运营稳定性达95.66%，具备直接应用于工业环境的能力。
原文摘要 · Abstract (English)
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.
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