Rotor Failure Diagnosis of Induction Motors by Wavelet Transform and Fourier Transform in Function of the Load

被引：0

作者：

da Costa, Cesar ^{[1
]}

Kashiwagi, Masamori ^{[2
]}

Mathias, Mauro Hugo ^{[2
]}

机构：

[1] IFSP Fed Inst Educ, Electrotech Engn, BR-01109010 Sao Paulo, SP, Brazil

[2] Univ Estadual Paulista, UNESP, Mech Engn, Sao Paulo, SP, Brazil

来源：

INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND ARTIFICIAL INTELLIGENCE (ICCSAI 2014) | 2015年

关键词：

Rotating electrical machine; Diagnostic; Digital signal processing; EMPIRICAL MODE DECOMPOSITION; FAULT-DIAGNOSIS; FEATURE-EXTRACTION; PACKET TRANSFORM; CLASSIFICATION; MACHINE; BARS;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents two diagnostic methods for the online detection of broken bars in induction motors with squirrel-cage type rotors. The wavelet representation of a function is a new technique. Wavelet transform of a function is the improved version of Fourier transform. Fourier transform is a powerful tool for analyzing the components of a stationary signal. But it is failed for analyzing the non-stationary signal whereas wavelet transform allows the components of a non-stationary signal to be analyzed. In this paper, our main goal is to find out the advantages of wavelet transform compared to Fourier transform in rotor failure diagnosis of induction motors.

引用

页码：109 / 113

页数：5

共 10 条

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[8] The fault feature extraction and classification of gear using principal component analysis and kernel principal component analysis based on the wavelet packet transform
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[9] A new diagnosis of broken rotor bar fault extent in three phase squirrel cage induction motor
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[J]. MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2014, 42 (1-2) : 388 - 403
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