Distributional Reinforcement Learning for Condition-Based Maintenance of Multi-Pump Equipment
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 11 of its 28 references that are in the map. First posted on arXiv 19 Jan 2026.
Builds on
11 of 28 references.
References · 11
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Citing papers per year: 2026: 1.
1 paper.