Model-based fault diagnosis in electric drives using machine learning

• Published in: IEEE/ASME transactions on mechatronics Vol. 11; no. 3; pp. 290 - 303
• Main Authors: Yi Lu Murphey, Masrur, M.A., ZhiHang Chen, Baifang Zhang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-06-2006; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial intelligence; Automotive engineering; Computer simulation; Diagnostic systems; Electric drives; Electric motors; Electric power generation; electric vehicle; Electrical fault detection; Electronics industry; Fault diagnosis; Faults; field-oriented control (FOC); fuzzy techniques; hybrid vehicle; inverter; Inverters; Machine learning; model-based diagnostics; motor; neural network; Neural networks; Power electronics; Power system modeling; Studies
• ISSN: 1083-4435; 1941-014X
• DOI: 10.1109/TMECH.2006.875568

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 a 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 the 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 the 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 a correct classification of faults over a wide operating domain.