Identification of significant intrinsic mode functions for the diagnosis of induction motor fault

被引：13

作者：

Cho, Sangjin ^{[1
]}

Shahriar, Md Rifat ^{[1
]}

Chong, Uipil ^{[1
]}

机构：

[1] Univ Ulsan, Sch Elect Engn, Ulsan 680749, South Korea

来源：

JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA | 2014年 / 136卷 / 02期

关键词：

DECOMPOSITION; SPECTRUM;

D O I：

10.1121/1.4885541

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

For the analysis of non-stationary signals generated by a non-linear process like fault of an induction motor, empirical mode decomposition (EMD) is the best choice as it decomposes the signal into its natural oscillatory modes known as intrinsic mode functions (IMFs). However, some of these oscillatory modes obtained from a fault signal are not significant as they do not bear any fault signature and can cause misclassification of the fault instance. To solve this issue, a novel IMF selection algorithm is proposed in this work. (C) 2014 Acoustical Society of America

引用

页码：EL72 / EL77

页数：6

共 13 条

[1] Advances in Diagnostic Techniques for Induction Machines
Bellini, Alberto
Filippetti, Fiorenzo
Tassoni, Carta
Capolino, Gerard-Andre
[J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2008, 55 (12) : 4109 - 4126
[2] Cheng JS, 2009, SHOCK VIB, V16, P89, DOI [10.3233/SAV-2009-0457, 10.1155/2009/519502]
[3] Machine fault feature extraction based on intrinsic mode functions
Fan, Xianfeng
Zuo, Ming J.
[J]. MEASUREMENT SCIENCE AND TECHNOLOGY, 2008, 19 (04)
[4] Empirical mode decomposition as a filter bank
Flandrin, P
Rilling, G
Gonçalvés, P
[J]. IEEE SIGNAL PROCESSING LETTERS, 2004, 11 (02) : 112 - 114
[5] A comparison of methods for multiclass support vector machines
Hsu, CW
Lin, CJ
[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS, 2002, 13 (02): : 415 - 425
[6] The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
Huang, NE
Shen, Z
Long, SR
Wu, MLC
Shih, HH
Zheng, QN
Yen, NC
Tung, CC
Liu, HH
[J]. PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, 1998, 454 (1971): : 903 - 995
[7] Hilbert-Huang transform and marginal spectrum for detection and diagnosis of localized defects in roller bearings
Li, Hui
Zhang, Yuping
Zheng, Haiqi
[J]. JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY, 2009, 23 (02) : 291 - 301
[8] Fault diagnosis of induction motors utilizing local binary pattern-based texture analysis
Shahriar, Md Rifat
Ahsan, Tanveer
Chong, UiPil
[J]. EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING, 2013,
[9] ON INTRINSIC MODE FUNCTION
Wang, Gang
Chen, Xian-Yao
Qiao, Fang-Li
Wu, Zhaohua
Huang, Norden E.
[J]. ADVANCES IN DATA SCIENCE AND ADAPTIVE ANALYSIS, 2010, 2 (03) : 277 - 293
[10] Support vector machine in machine condition monitoring and fault diagnosis
Widodo, Achmad
Yang, Bo-Suk
[J]. MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2007, 21 (06) : 2560 - 2574

← 1 2 →