Model-Based Fault Diagnosis 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 model based fault diagnostics system developed using machine learning technology for detecting and locating multiple classes of faults in an electric drive. Power electronics inverter can be considered to be the weakest link in such a system from hardware failure point of view, hence this work is focused on detecting faults and finding which switches in the inverter cause the faults. A simulation model has been developed based on the theoretical foundations of electric drives to simulate normal condition, all single-switch and post-short-circuit faults. 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 validated the capability of FDNN on data generated by an experimental bench setup. Our research demonstrates that with a robust machine learning approach, a diagnostic system can be trained based on a simulated electric drive model which can lead to correct classification of faults over a wide operating domain.