A Deep Reinforcement Learning Approach for Predictive Maintenance in Edge-Enabled Sensor Systems

Zahmatkeshan, Ladan; Javadi Noshabadi, Alireza; Abdollahzadeh, Aliakbar; Talesh Hosseini, Sajjad

doi:10.22060/eej.2026.24815.5769

A Deep Reinforcement Learning Approach for Predictive Maintenance in Edge-Enabled Sensor Systems

Articles in Press

Document Type : Research Article

Authors

Ladan Zahmatkeshan ¹

Alireza Javadi Noshabadi ¹

Aliakbar Abdollahzadeh ²

Sajjad Talesh Hosseini ³

¹ Department of Mining Engineering, University of Kashan, Kashan, Iran

² Department of Mining Engineering, Amirkabir University of Technology, Tehran, Iran

³ Department of Mining Engineering, Faculty of Engineering, Imam Khomeini International University, Qazvin, Iran

10.22060/eej.2026.24815.5769

Abstract

Unexpected failures in essential industrial systems can cause operational disruptions and financial losses. To mitigate unplanned downtime and maintain safe, efficient functioning of critical assets, predictive maintenance strategies are essential. However, with the rapid increase in sensor-equipped machinery, the overwhelming volume of generated data has outpaced the capabilities of traditional machine learning models to provide accurate, real-time diagnostics. This research introduces a model-free deep reinforcement learning (DRL) approach tailored for predictive maintenance within sensor-integrated equipment networks. Each machine is equipped with a sensor module that captures real-time data and detects anomalies. Unlike conventional opaque regression-based methods, the proposed framework autonomously determines optimal maintenance policies and delivers actionable insights for each individual device. Experimental evaluations indicate the potential of this adaptive learning method to extend across diverse maintenance scenarios.

Keywords

Predictive Maintenance

Deep Reinforcement Learning (DRL)

Edge-Enabled Sensor Systems

Anomaly Detection

Intelligent Industrial Systems

Subjects