Analyzing Motor Health using Sensors and Oscilloscopes Enables Predictive Analysis

Abstract:
Monitoring the conditions of Industrial, EV, and BLDC motors is crucial for system reliability and efficiency. This study explores using various sensors, such as vibration, magnetometers, temperature, voltage, and current sensors, to gather real-time data for developing robust machine learning (ML) models for predictive maintenance. The data is pre-processed to remove noise and irrelevant information, followed by feature extraction to identify key indicators of motor health. This paper highlights the integration of artificial intelligence (AI) with real motors that allows for continuous learning, improved predictive accuracy, detection of faults and seamless interaction between sensors and ML models for real-time monitoring and decision-making.