Presentation Description: Motor condition monitoring is essential for maintaining the reliability and efficiency of industrial systems. This study investigates the use of various sensors, including vibration, magnetometers, temperature, voltage, and current sensors, to monitor motor conditions. By capturing real-time data from these sensors, we aim to develop robust machine learning (ML) models for predictive maintenance. The sensor data is collected and pre-processed to eliminate noise and irrelevant information. Feature extraction techniques are applied to identify key indicators of motor health. These features are then used to train ML models that can predict potential failures and estimate the remaining useful life of the motors. The integration of artificial intelligence (AI) enables the system to continuously learn and improve its predictive accuracy over time. This talk demonstrates the potential of combining sensor technology with AI/ML modelling and oscilloscopes to revolutionize motor condition monitoring and predictive maintenance.
