Probabilistic frequency-domain discrete wavelet transform for better detection of bearing faults in induction motors

被引:37
作者
Ghods, Amirhossein [1 ]
Lee, Hong-Hee [2 ]
机构
[1] Univ Ulsan, Dept Elect & Comp Engn, Ulsan 680749, South Korea
[2] Univ Ulsan, Sch Elect Engn, Ulsan 680749, South Korea
基金
新加坡国家研究基金会;
关键词
Frequency-domain discrete wavelet transform; Fault diagnosis; Inner/outer race bearing faults; Monte-Carlo modeling; Stochastic modeling; VIBRATION; ELEMENT; STATOR;
D O I
10.1016/j.neucom.2015.06.100
中图分类号
TP18 [人工智能理论];
学科分类号
140502 [人工智能];
摘要
Due to the importance of induction motors' continuous operation, early detection of faults has become a major trend. As reported in an IEEE study, bearing failures include more than half of mechanical faults. To detect existence of this fault, methods such as (short-time) Fourier, (continuous-discrete) wavelet, and Park transforms introduced. Static modeling of fault behavior is determined to be the major deficiency of above-mentioned methods. In other words, using conventional detection techniques, fault is assumed to have deterministic behavior, in which the fault frequencies are constant. As a matter of fact, fault characteristics can be affected under loading or environmental conditions, which makes conventional standing invalid. Authors of this paper have developed their previously introduced technique, frequency domain discrete wavelet transform (FD-DWT) into a stochastic model. This makes the detection process valid for more variety of fault conditions and leads to earlier detection of fault and less damage to motor compared to other strategies. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码:206 / 216
页数:11
相关论文
共 26 条
[1]
Temporary short circuit detection in induction motor winding using combination of wavelet transform and neural network [J].
Asfani, D. A. ;
Muhammad, A. K. ;
Syafaruddin ;
Purnomo, M. N. ;
Hiyama, T. .
EXPERT SYSTEMS WITH APPLICATIONS, 2012, 39 (05) :5367-5375
[2]
A review of induction motors signature analysis as a medium for faults detection [J].
Benbouzid, ME .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2000, 47 (05) :984-993
[3]
Models for bearing damage detection in induction motors using stator current monitoring [J].
Bloedt, Martin ;
Granjon, Pierre ;
Raison, Bertrand ;
Rostaing, Gilles .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2008, 55 (04) :1813-1822
[4]
Burrus C.S, 2015, Introduction to wavelets and wavelet transforms. A primer
[5]
Dampierwhetham W.C., 2009, FAMILY NATION STUDY
[6]
Duque O., 2005, International Electric Machines and Drives Conference (IEEE Cat. No.05EX1023C), P17, DOI 10.1109/IEMDC.2005.195695
[7]
Novel indices for broken rotor bars fault diagnosis in induction motors using wavelet transform [J].
Ebrahimi, Bashir Mandi ;
Fair, Jawad ;
Lotfi-Fard, S. ;
Pillay, P. .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2012, 30 :131-145
[8]
Bearing damage detection via wavelet packet decomposition of the stator current [J].
Eren, L ;
Devaney, MJ .
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2004, 53 (02) :431-436
[9]
Eren L., 2004, Proceedings of the 21st IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No.04CH37510), P1657, DOI 10.1109/IMTC.2004.1351399
[10]
Detection of Stator, Bearing and Rotor Faults in Induction Motors [J].
Ergin, Semih ;
Uzuntas, Arzu ;
Gulmezoglu, M. Bilginer .
INTERNATIONAL CONFERENCE ON COMMUNICATION TECHNOLOGY AND SYSTEM DESIGN 2011, 2012, 30 :1103-1109