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Zahmatkeshan, Ladan, Javadi Noshabadi, Alireza, Abdollahzadeh, Aliakbar, Talesh Hosseini, Sajjad. (1404). A Deep Reinforcement Learning Approach for Predictive Maintenance in Edge-Enabled Sensor Systems. نشریه مهندسی عمران امیرکبیر, (), -. doi: 10.22060/eej.2026.24815.5769
Ladan Zahmatkeshan; Alireza Javadi Noshabadi; Aliakbar Abdollahzadeh; Sajjad Talesh Hosseini. "A Deep Reinforcement Learning Approach for Predictive Maintenance in Edge-Enabled Sensor Systems". نشریه مهندسی عمران امیرکبیر, , , 1404, -. doi: 10.22060/eej.2026.24815.5769
Zahmatkeshan, Ladan, Javadi Noshabadi, Alireza, Abdollahzadeh, Aliakbar, Talesh Hosseini, Sajjad. (1404). 'A Deep Reinforcement Learning Approach for Predictive Maintenance in Edge-Enabled Sensor Systems', نشریه مهندسی عمران امیرکبیر, (), pp. -. doi: 10.22060/eej.2026.24815.5769
Zahmatkeshan, Ladan, Javadi Noshabadi, Alireza, Abdollahzadeh, Aliakbar, Talesh Hosseini, Sajjad. A Deep Reinforcement Learning Approach for Predictive Maintenance in Edge-Enabled Sensor Systems. نشریه مهندسی عمران امیرکبیر, 1404; (): -. doi: 10.22060/eej.2026.24815.5769

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

AUT Journal of Electrical Engineering
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 03 اسفند 1404    اصل مقاله (1.33 M)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22060/eej.2026.24815.5769
نویسندگان
Ladan Zahmatkeshan1؛ Alireza Javadi Noshabadi* 1؛ Aliakbar Abdollahzadeh2؛ Sajjad Talesh Hosseini3
1Department of Mining Engineering, University of Kashan, Kashan, Iran
2Department of Mining Engineering, Amirkabir University of Technology, Tehran, Iran
3Department of Mining Engineering, Faculty of Engineering, Imam Khomeini International University, Qazvin, Iran
چکیده
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.
کلیدواژه‌ها
Predictive Maintenance؛ Deep Reinforcement Learning (DRL)؛ Edge-Enabled Sensor Systems؛ Anomaly Detection؛ Intelligent Industrial Systems
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