Fault diagnostics in electric drives using machine learning

Abstract

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. © Springer-Verlag Berlin Heidelberg 2006.