Bias reduction for reliable fault detection of electric motors under measurement noise of non-zero means
by Dusan Progovac; Le Yi Wang; George Yin
International Journal of Modelling, Identification and Control (IJMIC), Vol. 22, No. 1, 2014

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.

Online publication date: Sat, 27-Sep-2014

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