Reinforcement and Deep Reinforcement Learning-based Solutions for Machine Maintenance Planning, Scheduling Policies, and Optimization

TL;DR

This paper reviews how reinforcement and deep reinforcement learning techniques are applied to develop optimal maintenance planning and scheduling policies, aiming to reduce costs and improve system reliability.

Contribution

It provides a comprehensive taxonomy and classification of existing reinforcement learning applications in maintenance, along with summarized methodologies, findings, and future research directions.

Findings

Reinforcement learning effectively optimizes maintenance policies.

Deep reinforcement learning enhances decision-making in complex systems.

Identified research gaps for future exploration.

Abstract

Systems and machines undergo various failure modes that result in machine health degradation, so maintenance actions are required to restore them back to a state where they can perform their expected functions. Since maintenance tasks are inevitable, maintenance planning is essential to ensure the smooth operations of the production system and other industries at large. Maintenance planning is a decision-making problem that aims at developing optimum maintenance policies and plans that help reduces maintenance costs, extend asset life, maximize their availability, and ultimately ensure workplace safety. Reinforcement learning is a data-driven decision-making algorithm that has been increasingly applied to develop dynamic maintenance plans while leveraging the continuous information from condition monitoring of the system and machine states. By leveraging the condition monitoring data of systems and machines with reinforcement learning, smart maintenance planners can be developed, which is a precursor to achieving a smart factory. This paper presents a literature review on the applications of reinforcement and deep reinforcement learning for maintenance planning and optimization problems. To capture the common ideas without losing touch with the uniqueness of each publication, taxonomies used to categorize the systems were developed, and reviewed publications were highlighted, classified, and summarized based on these taxonomies. Adopted methodologies, findings, and well-defined interpretations of the reviewed studies were summarized in graphical and tabular representations to maximize the utility of the work for both researchers and practitioners. This work also highlights the research gaps, key insights from the literature, and areas for future work.