Fault diagnostics in electric drives using machine learning

Electric motor and power electronics based inverter are the major components in industrial and automotive electric drives. In this paper we present a fault diagnostics system developed using machine learning technology for detecting and locating multiple classes of faults in an electric drive. A machine learning algorithm has been developed to automatically select a set of representative operating points in the (torque, speed) domain, which in turn is sent to the simulated electric drive model to generate signals for the training of a diagnostic neural network, "Fault Diagnostic Neural Network" (FDNN). We presented our study on two different neural network systems and show that a well-designed hierarchical neural network system is robust in detecting and locating faults in electric drives.