Title: Bias reduction for reliable fault detection of electric motors under measurement noise of non-zero means
Authors: Dusan Progovac; Le Yi Wang; George Yin
Addresses: Delphi Corp., 3000 University Drive, Auburn Hills, MI 48326, USA ' Department of Electrical and Computer Engineering, Wayne State University, Detroit, MI 48202, USA ' Department of Mathematics, Wayne State University, Detroit, MI 48202, USA
Abstract: Model identification and fault detection are of importance for reliable motor control. For fault diagnosis, motor parameters must be estimated accurately and reliably during operation. Typical measurement noises for power electronics encounter unknown and drifting non-zero means. This causes identification bias and has detrimental effects on reliability of fault detection. Based on enhanced model structures of electric motors that accommodate both normal and faulty modes, this paper introduces algorithms that correct this bias and restore diagnosis accuracy. BLDC motors are used as a benchmark type for concrete algorithm development and evaluation. Algorithms are presented, their properties are established, and their accuracy and robustness are evaluated by case studies.
Keywords: electrical machines; parameter estimation; fault detection; BLDC motors; noise; bias reduction; electric motors; model identification; motor control; power electronics; non-zero means.
International Journal of Modelling, Identification and Control, 2014 Vol.22 No.1, pp.1 - 12
Published online: 27 Sep 2014 *Full-text access for editors Access for subscribers Purchase this article Comment on this article