System and method for motor fault detection using hybrid physics-based and data-driven method

Abstract

A method for motor fault detection using hybrid physics based and data driven method comprises collecting synthetic data representing fault conditions of the motor using a physics-based simulation model of a type of the motor. The method further comprises collecting a batch of real-world data representing measurements of the operations of the motor including operations of the motor under normal conditions. Domain adaptation is applied to align a distribution of features in the synthetic data with a distribution of features in the batch of real-world data. The method further comprises training a machine learning model using the domain-adapted synthetic data. The faults conditions are detected in the motor by applying the trained machine learning model to real-time operational data measured during the operations of the electric motor.

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