APPLY ARTIFICIAL INTELLIGENCE (AI) AND THE INTERNET OF THINGS (IoT) FOR MONITORING AND DIAGNOSING ELECTRICAL ENGINE FAULTS

Abstract

This paper focuses on the application of artificial intelligence (AI) and Internet of Things (IoT) in monitoring and
diagnosing faults in electric motors, aiming to optimize the management and maintenance processes of electrical
systems, especially in industries such as manufacturing, transportation, and energy. The article emphasizes the
capability of AI and IoT in automating fault monitoring and diagnosis. The main signals utilized are vibration and
temperature on the motor casing, and by integrating information from these sensors, the AI system can detect and
alert to potential issues or conditions that may not be identifiable through traditional testing methods. Additionally,
the paper tests three popular machine learning and AI models - YOLO (You Only Look Once), SVM (Support Vector
Machine), and ResNet (Residual Neural Network) - to evaluate their accuracy based on a custom-built dataset. This
helps assess the performance of these models in identifying and classifying fault conditions in electric motors.