Motor shaft misalignment detection using multiscale entropy with wavelet denoising

被引：57

作者：

Lin, Jun-Lin ^{[1
]}

Liu, Julie Yu-Chih ^{[1
]}

Li, Chih-Wen ^{[1
]}

Tsai, Li-Feng ^{[2
,3
]}

Chung, Hsin-Yi ^{[2
,3
]}

机构：

[1] Yuan Ze Univ, Dept Informat Management, Chungli 32003, Taiwan

[2] Ind Technol Res Inst, Energy Lab, Hsinchu 310, Taiwan

[3] Ind Technol Res Inst, Environm Res Lab, Hsinchu 310, Taiwan

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2010年 / 37卷 / 10期

关键词：

Multiscale entropy; Wavelet transform; Induction motor; Fault detection; APPROXIMATE ENTROPY; INDUCTION-MOTORS; STATOR CURRENT; FAULTS; VIBRATION;

D O I：

10.1016/j.eswa.2010.04.009

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Misalignment of motor shaft (also manifesting as static eccentricity) is a common motor fault resulting from improper installation or damage of the machine components and their support structure. Spectrum analysis is generally used for online detection of such faults. This study presents a novel approach to discover features that distinguish the vibration signals of a normal motor from those of a misaligned one. These features are obtained from the difference of multiscale entropy of a signal, before and after the signal is denoised using wavelet transform. Experimental results show that classifiers based on these features obtain better and more stable accuracy rates than those based on frequency-related features. (C) 2010 Elsevier Ltd. All rights reserved.

引用

页码：7200 / 7204

页数：5

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