An IoT and Machine Learning-Based Predictive Maintenance System for Electrical Motors
Noor A. Mohammed, Osamah F. Abdulateef, Ali H. Hamad
Abstract
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 communication between the sensors, gateway devices, and the cloud server.The system was tested on an operational motors dataset, five machine learning algorithms, namely k-nearest neighbor (KNN), supported vector machine (SVM), random forest (RF), linear regression (LR), and naive bayes (NB), are used to analyze and process the collected data to predict motor failures and offer maintenance recommendations.Results demonstrate the random forest model achieves the highest accuracy in failure prediction.The solution minimizes downtime and costs through optimized maintenance schedules and decisions.It represents an Industry 4.0 approach to sustainable smart manufacturing.