Extraction of induction motor fault characteristics in frequency domain and fuzzy entropy

Feature extraction for fault detection of an induction motor is carried out using the information of stator current. After preprocessing actual data, Fourier and wavelet transforms are applied to detect characteristics under the healthy and various faulted conditions. The most reliable phase current among 3-phase currents is selected by the fuzzy entropy. The fuzzy membership function is also required to obtain the fuzzy entropy. The membership function is designed by the bootstrap method and central limit theorem. PCA (principal component analysis) and LDA (linear discriminant analysis) is finally applied to obtain characteristics of the healthy and faulted motors

[1]  George J. Vachtsevanos,et al.  A polynomial fuzzy neural network for identification and control , 1996, Proceedings of North American Fuzzy Information Processing.

[2]  Angel R. Martinez,et al.  Computational Statistics Handbook with MATLAB , 2001 .

[3]  G. B. Kliman,et al.  Noninvasive detection of broken rotor bars in operating induction motors , 1988 .

[4]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[5]  G. D. Bergland,et al.  A guided tour of the fast Fourier transform , 1969, IEEE Spectrum.

[6]  Rolf Isermann.  FAULT DIAGNOSIS OF MACHINES VIA PARAMETER ESTIMATION AND KNOWLEDGE PROCESSING , 1992 .

[7]  R. Bracewell.  The Fourier Transform and Its Applications , 1966 .

[8]  Ronald N. Bracewell,et al.  The Fourier Transform and Its Applications , 1966 .

[9]  W. T. Thomson,et al.  Online current monitoring for fault diagnosis in inverter fed induction motors , 1988 .

[10]  K. W. Cattermole.  The Fourier Transform and its Applications , 1965 .

[11]  A. G. Ivakhnenko,et al.  Polynomial Theory of Complex Systems , 1971, IEEE Trans. Syst. Man Cybern..

[12]  Hamid A. Toliyat,et al.  Condition monitoring and fault diagnosis of electrical machines-a review , 1999, Conference Record of the 1999 IEEE Industry Applications Conference. Thirty-Forth IAS Annual Meeting (Cat. No.99CH36370).

[13]  Liu Xuecheng,et al.  Entropy, distance measure and similarity measure of fuzzy sets and their relations , 1992 .

[14]  Weixin Xie,et al.  On some properties of distance measures , 2001, Fuzzy Sets Syst..

[15]  Jafar Milimonfared,et al.  Broken bar detection in induction motor via wavelet transformation , 2001, IECON'01. 27th Annual Conference of the IEEE Industrial Electronics Society (Cat. No.37243).

[16]  S. Mallat.  A wavelet tour of signal processing , 1998 .

[17]  Rolf Isermann,et al.  Fault diagnosis of machines via parameter estimation and knowledge processing - Tutorial paper , 1991, Autom..

[18]  J. Penman,et al.  Detection and location of interturn short circuits in the stator windings of operating motors , 1994 .

[19]  G.E. Dawson,et al.  The detection of broken bars in the cage rotor of an induction machine , 1988, Conference Record of the 1988 IEEE Industry Applications Society Annual Meeting.

[20]  H.A. Toliyat,et al.  Pattern recognition-a technique for induction machines rotor fault detection "eccentricity and broken bar fault" , 2001, Conference Record of the 2001 IEEE Industry Applications Conference. 36th IAS Annual Meeting (Cat. No.01CH37248).

[21]  Peter Vas,et al.  Parameter Estimation, Condition Monitoring, and Diagnosis of Electrical Machines , 1993 .

[22]  Alan S. Willsky,et al.  A survey of design methods for failure detection in dynamic systems , 1976, Autom..

[23]  Birsen Yazici,et al.  An adaptive statistical time-frequency method for detection of broken bars and bearing faults in motors using stator current , 1999 .

[24]  Duc Truong Pham,et al.  Neural Networks for Identification, Prediction and Control , 1995 .