Statistic Moment Based Method for the Detection and Diagnosis of Induction Motor Stator Fault

In this paper a new algorithm for the detection of a three-phase induction motor stator fault is presented. Several fault detection methods are based on the analysis of the input current Park's vector. This diagnostic technique is based on the identification of a specified current pattern obtained from the transformation of the three-phase stator currents to an equivalent two-phase system. This new algorithm proposes a pattern recognition method to identify induction motor stator faults. The proposed fault diagnosis system is based on the statistic moment method, and also indicates the extension of the stator fault. Simulation and experimental results are presented in order to verify the effectiveness of the proposed method.

[1]  Peter Tavner,et al.  Condition monitoring of electrical machines , 1987 .

[2]  Gerald Burt Kliman,et al.  Methods of Motor Current Signature Analysis , 1992 .

[3]  Jan Flusser,et al.  Pattern recognition by affine moment invariants , 1993, Pattern Recognit..

[4]  Thomas G. Habetler,et al.  An unsupervised, on-line system for induction motor fault detection using stator current monitoring , 1994, Proceedings of 1994 IEEE Industry Applications Society Annual Meeting.

[5]  D.S.B. Fonseca,et al.  Inter-turn stator winding fault diagnosis in three-phase induction motors, by Park's Vector approach , 1997, 1997 IEEE International Electric Machines and Drives Conference Record.

[6]  Jan Flusser,et al.  Affine moment invariants: a new tool for character recognition , 1994, Pattern Recognit. Lett..

[7]  Antonio J. Marques Cardoso,et al.  Inter-turn stator winding fault diagnosis in three-phase induction motors, by Park's Vector approach , 1997 .

[8]  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).

[9]  Dana H. Ballard,et al.  Computer Vision , 1982 .

[10]  Mohamed Benbouzid,et al.  Induction motor stator faults diagnosis by a current Concordia pattern-based fuzzy decision system , 2003 .

[11]  Mohamed El Hachemi Benbouzid.  A review of induction motors signature analysis as a medium for faults detection , 2000, IEEE Trans. Ind. Electron..

[12]  A.J. Marques Cardoso,et al.  Rotor cage fault diagnosis in three-phase induction motors, by Park's vector approach , 1995, IAS '95. Conference Record of the 1995 IEEE Industry Applications Conference Thirtieth IAS Annual Meeting.