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Distributional Reinforcement Learning for Condition-Based Maintenance of Multi-Pump Equipment

Takato Yasuno

TL;DR

This work addresses CBM for fleets of pumps by formulating the problem as an MDP and solving it with a distributional QR-DQN that incorporates aging factors. It introduces a five-component reward and a scalable, industrially adaptable network with dueling heads and Noisy Nets, enabling risk-sensitive decisions across three strategies: Safety-First, Balanced, and Cost-Efficient. Empirical results on a three-pump testbed show substantial economic and reliability benefits, notably ROI $=3.91$, stability around $95\%$, and clear trade-offs between speed of convergence and long-term adaptability. The study also contributes an CV-based stability assessment and an industrial deployment framework, with open-source code to facilitate practical adoption in diverse sectors.

Abstract

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.

Distributional Reinforcement Learning for Condition-Based Maintenance of Multi-Pump Equipment

TL;DR

This work addresses CBM for fleets of pumps by formulating the problem as an MDP and solving it with a distributional QR-DQN that incorporates aging factors. It introduces a five-component reward and a scalable, industrially adaptable network with dueling heads and Noisy Nets, enabling risk-sensitive decisions across three strategies: Safety-First, Balanced, and Cost-Efficient. Empirical results on a three-pump testbed show substantial economic and reliability benefits, notably ROI , stability around , and clear trade-offs between speed of convergence and long-term adaptability. The study also contributes an CV-based stability assessment and an industrial deployment framework, with open-source code to facilitate practical adoption in diverse sectors.

Abstract

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.
Paper Structure (27 sections, 10 equations, 15 figures, 1 table)

This paper contains 27 sections, 10 equations, 15 figures, 1 table.

Table of Contents

  1. Introduction
  2. Industrial Motivation and Research Contributions
  3. Related Work
  4. Contemporary Survey Literature and Research Context
  5. Distributional Reinforcement Learning and Quantile Regression
  6. Advanced Deep RL Techniques
  7. Predictive Maintenance and Deep Learning
  8. Industrial Applications and Case Studies
  9. Risk-Sensitive and Anomaly Detection Approaches
  10. Research Contribution and Positioning
  11. Methodology
  12. Problem Formulation
  13. Deep Q-Network Architecture with Industrial Adaptability
  14. Experimental Results
  15. Industrial Equipment Testbed
  16. ...and 12 more sections

Figures (15)

  • Figure 1: Overview of Application Methodology (Created using NotebookLM)
  • Figure 2: DQN Learning Process for Multi-Pump CBM System
  • Figure 3: State-Action Value Computation Process
  • Figure 4: Safety-First Strategy Training Analysis (3000 Episodes)
  • Figure 5: Safety-First Strategy Reward Distribution Analysis
  • ...and 10 more figures