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Parameter estimation and reliable fault detection of electric motors

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Abstract

Accurate model identification and fault detection are necessary for reliable motor control. Motor-characterizing parameters experience substantial changes due to aging, motor operating conditions, and faults. Consequently, motor parameters must be estimated accurately and reliably during operation. Based on enhanced model structures of electric motors that accommodate both normal and faulty modes, this paper introduces bias-corrected least-squares (LS) estimation algorithms that incorporate functions for correcting estimation bias, forgetting factors for capturing sudden faults, and recursive structures for efficient real-time implementation. Permanent magnet 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 simulation case studies under both normal operations and inter-turn winding faults. Implementation issues from different motor control schemes are also discussed.

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Authors and Affiliations

  1. Delphi Corporation, 3000 University Drive, Auburn Hills, MI, 48326, USA

    Dusan Progovac

  2. Department of Electrical and Computer Engineering, Wayne State University, Detroit, MI, 48202, USA

    Le Yi Wang

  3. Department of Mathematics, Wayne State University, Detroit, MI, 48202, USA

    George Yin

Authors
  1. Dusan Progovac
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  2. Le Yi Wang
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  3. George Yin
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Corresponding author

Correspondence to Le Yi Wang.

Additional information

Dusan PROGOVAC received the B.S. degree in Electrical Engineering in 1988 and his M.S. degree in Mathematics in 1987 all from University of Southern California, Los Angeles. Since 1988, he has been working as Senior Engineering Specialist for General Dynamics Land System, Senior Project Engineer for TRW, Project Design Engineer at Ford Motor Company, and Software Engineer at Delphi Corporation. He also worked as Associate Lecturer for Department of Mathematics, University of Wisconsin at Milwaukee. His research interests are in the areas of information complexity, system identification, detection of abrupt changes, fault detection and vehicle powertrain control systems. He presented his papers at several conferences and he has been Program Committee Member for International Conferences.

Le Yi WANG received the Ph.D. degree in Electrical Engineering from McGill University, Montreal, Canada, in 1990. Since 1990, he has been with Wayne State University, Detroit, Michigan, where he is currently a professor in the Department of Electrical and Computer Engineering. His research interests are in the areas of complexity and information, system identification, robust control, H∞ optimization, time-varying systems, adaptive systems, hybrid and nonlinear systems, information processing and learning, as well as medical, automotive, communications, power systems, and computer applications of control methodologies. He was a keynote speaker in several international conferences. He was an associate editor of the IEEE Transactions on Automatic Control and several other journals, and currently is an associate editor of the Journal of System Sciences and Complexity and Control Theory and Technology. He is a Fellow of IEEE.

George YIN joined Wayne State University in 1987 and became a professor in 1996. Working on stochastic systems, he is Chair of SIAM Activity Group in Control and Systems Theory and is one of the Board of Directors of American Automatic Control Council. He was Co-Chair of SIAM Conference on Control & Its Application, 2011, Co-Chair of 1996 AMS-SIAM Summer Seminar and 2003 AMS-IMS-SIAM Summer Research Conference, Coorganizer of 2005 IMA Workshop on Wireless Communications. He chaired the SIAM W.T. and Idalia Reid Prize Committee, the SIAG/Control and Systems Theory Prize Committee, and the SIAM SICON Best Paper Prize Committee. He is an associate editor of Control Theory and Technology, SIAM Journal on Control and Optimization, and on the editorial board of many other journals and book series. He was an associate editor of Automatica and IEEE T-AC. He was President of Wayne State University’s Academy of Scholars. He is a Fellow of IEEE.

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Progovac, D., Wang, L.Y. & Yin, G. Parameter estimation and reliable fault detection of electric motors. Control Theory Technol. 12, 110–121 (2014). https://doi.org/10.1007/s11768-014-0178-y

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  • DOI: https://doi.org/10.1007/s11768-014-0178-y

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