Diagnosis of short circuit faults in stator winding of motor based on Hidden Markov Mortal

被引：1

作者：

TOENEC Corporation, 1-79, Takiharu-cho, Minami-ku, Nagoya 457-0819 ^{[1
]}

不详 ^{[2
]}

不详 ^{[3
]}

不详 ^{[4
]}

不详 ^{[5
]}

不详 ^{[6
]}

机构：

[1] TOENEC Corporation, Minami-ku, Nagoya 457-0819, 1-79, Takiharu-cho

[2] Nagare College, Nagoya Institute of Technology, Showa-ku, Nagoya 466-8555, Gokiso-cho

[3] Dept. of Mechanical Science and Engineering, Nagoya University, Chikusa-ku, Nagoya 464-8603, Furo-cho

来源：

IEEJ Trans. Power Energy | 2006年 / 9卷 / 917-925期

关键词：

Fault diagnosis; Hidden Markov Model; Motor; Turn to turn short circuit fault;

D O I：

10.1541/ieejpes.126.917

中图分类号：

学科分类号：

摘要：

This paper proposes a new diagnosis method for short circuit faults in stator winding of motor based on Hidden Markov Model. Short circuit fault of a motor is one of the most probable faults in motor drive systems. When the fault occurs, the current waveform running in the motor is no longer sinusoidal which is observed in the healthy motor. The variation of the waveform in the faulty case depends on the location and degree of short circuit fault in the winding. In this paper, a Hidden Markov Model (HMM), which is widely used in the field of speech recognition, is exploited to capture and recognize the variation in the faulty current waveform. Thanks to the similarity between the speech signal and the current waveform, the HMM is highly expected to work as a robust fault diagnoser. Finally, the usefulness of the proposed diagnosis method is verified through some experiments using real faulty current waveforms.

引用

页码：917 / 925

页数：8

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