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ISA Trans. 2018 Jun;77:179-187. doi: 10.1016/j.isatra.2018.03.029. Epub 2018 Apr 11.

On-line diagnosis of inter-turn short circuit fault for DC brushed motor.

Author information

1
Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, United States.
2
Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, United States. Electronic address: wei.zhan@tamu.edu.

Abstract

Extensive research effort has been made in fault diagnosis of motors and related components such as winding and ball bearing. In this paper, a new concept of inter-turn short circuit fault for DC brushed motors is proposed to include the short circuit ratio and short circuit resistance. A first-principle model is derived for motors with inter-turn short circuit fault. A statistical model based on Hidden Markov Model is developed for fault diagnosis purpose. This new method not only allows detection of motor winding short circuit fault, it can also provide estimation of the fault severity, as indicated by estimation of the short circuit ratio and the short circuit resistance. The estimated fault severity can be used for making appropriate decisions in response to the fault condition. The feasibility of the proposed methodology is studied for inter-turn short circuit of DC brushed motors using simulation in MATLAB/Simulink environment. In addition, it is shown that the proposed methodology is reliable with the presence of small random noise in the system parameters and measurement.

KEYWORDS:

DC motor; Hidden Markov Model; Inter-turn short circuit; Online fault diagnosis

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