An Integrated IoT–AI Framework for Real-Time Predictive Maintenance in Industrial Machinery

This paper presented an integrated IoT–AI framework for real-time predictive maintenance in industrial machinery. The proposed system combines IoT-based sensor monitoring, edge computing, cloud analytics, and Machine Learning algorithms to detect abnormal machine behavior and predict equipment failures before breakdown occurs. Experimental evaluation demonstrated prediction accuracy above 96% with high precision, recall, and reduced false alarm rates, confirming the effectiveness of the framework for intelligent industrial maintenance applications.   The integration of Random Forest, Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) models enabled accurate fault prediction and real-time monitoring under different industrial operating conditions. The proposed framework also reduced maintenance costs, minimized unplanned downtime, and improved operational efficiency compared with traditional reactive and preventive maintenance approaches.   Despite its advantages, the framework depends on reliable sensor connectivity, sufficient computational resources, and large training datasets for optimal performance. Future work will focus on improving scalability, enhancing resistance to noisy industrial environments, and integrating advanced deep learning techniques for more accurate fault prediction. The proposed IoT–AI framework demonstrates that the combination of intelligent analytics and real-time IoT monitoring provides an effective, scalable, and cost-efficient solution for predictive maintenance in Industry 4.0 and smart manufacturing environments.

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