Amirkabir University of Technology AUT Journal of Electrical Engineering 2588-2910 58 3 2026 07 01 A Deep Reinforcement Learning Approach for Predictive Maintenance in Edge-Enabled Sensor Systems 609 622 5995 10.22060/eej.2026.24815.5769 EN Ladan Zahmatkeshan Department of Mining Engineering, University of Kashan, Kashan, Iran Alireza Javadi Noshabadi Department of Mining Engineering, University of Kashan, Kashan, Iran 0000-0001-6239-7160 Aliakbar Abdollahzadeh Department of Mining Engineering, Amirkabir University of Technology, Tehran, Iran Sajjad Talesh Hosseini Department of Mining Engineering, Faculty of Engineering, Imam Khomeini International University, Qazvin, Iran 0009-0008-1720-0574 Journal Article 2025 09 26 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. https://eej.aut.ac.ir/article_5995_fcd4c889d516a54d5371f00e3fdd70dc.pdf