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The rise of Industry 4.0 and smart manufacturing has highlighted the importance of
utilizing intelligent manufacturing techniques, tools, and methods, including predictive
maintenance. This feature allows for the early identification of potential issues with
machinery, preventing them from reaching critical stages. This paper proposes an
intelligent predictive maintenance system for industrial equipment monitoring. The system
integrates Industrial IoT, MQTT messaging and machine learning algorithms. Vibration,
current and temperature sensors collect real-time data from electrical motors which is
analyzed using five ML models to detect anomalies and predict failures, enabling proactive
maintenance. The MQTT protocol is used for efficient com
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