Machine Learning based Fault Prediction in Single Phase Induction Motor

Detecting faults in induction motors before they completely stop working, fixing in an induction motor at the right time can prevent losses. A machine learning approach is proposed to detect faults in induction motors. Machine learning and condition monitoring techniques can help in detecting faults early. The approach uses algorithms to analyze vibration signals and learn important features from the frequency distribution of these signals. These features help understand the working status of the motor, including factors like current, voltage, speed, vibration and temperature. The information is then used in an IoT- based application for monitoring and updating. The approach combines feature automatically diagnose faults in motors. This helps in developing a system that can predict faults in a singlephase induction motor using various parameters and machine learning techniques. The system not only detects faults but also provides specific error codes, making it easier for technicians to quickly and accurately diagnose problems for efficient repairs.