Stator winding fault diagnosis in induction motors using the dq current trajectory mass center

This paper proposes the use of the park transformation mass center applied to the stator currents as a method for diagnosing the occurrence of stator winding faults in induction motor. Induction motor stator currents are first measured and recorded. Then, the park transform is applied to the obtained currents in order to obtain a specific pattern that allows the identification of the stator winding fault. For a healthy motor, a single point in a dq-plane is obtained. However, for an induction motor with some stator winding fault one obtains a circle, in the dq-plane, which is dependent on the fault severity. Accordingly to this relationship a fault severity is reported. In order to show the applicability of the proposed technique, several simulation and experimental results are presented.

[1]  V. Fernão Pires,et al.  Unsupervised Neural-Network-Based Algorithm for an On-Line Diagnosis of Three-Phase Induction Motor Stator Fault , 2007, IEEE Transactions on Industrial Electronics.

[2]  Mohamed Benbouzid,et al.  Monitoring and diagnosis of induction motors electrical faults using a current Park's vector pattern learning approach , 2000 .

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

[4]  Izzet Yilmaz,et al.  Induction Motor Bearing Failure Detection and Diagnosis: Park and Concordia Transform Approaches Comparative Study , 2008 .

[5]  Fiorenzo Filippetti,et al.  Recent developments of induction motor drives fault diagnosis using AI techniques , 2000, IEEE Trans. Ind. Electron..

[6]  H.A. Toliyat,et al.  DSP implementation of the multiple reference frames theory for the diagnosis of stator faults in a DTC induction motor drive , 2003, IEEE Transactions on Energy Conversion.

[7]  L.F.A. Pereira,et al.  Motor current signature analysis and fuzzy logic applied to the diagnosis of short-circuit faults in induction motors , 2005, 31st Annual Conference of IEEE Industrial Electronics Society, 2005. IECON 2005..

[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]  Gojko Joksimovic,et al.  The detection of inter-turn short circuits in the stator windings of operating motors , 2000, IEEE Trans. Ind. Electron..

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

[11]  V. Fernao Pires,et al.  Rotor cage fault diagnosis in three-phase induction motors based on a current and virtual flux approach , 2009 .

[12]  Bong-Hwan Kwon,et al.  Online Diagnosis of Induction Motors Using MCSA , 2006, IEEE Transactions on Industrial Electronics.

[13]  S. L. Ho,et al.  Detection of faults in induction motors using artificial neural networks , 1995 .

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

[15]  Tong Liu,et al.  Detection of stator turn fault in induction motors using the extension of multiple reference frames theory , 2005, 31st Annual Conference of IEEE Industrial Electronics Society, 2005. IECON 2005..

[16]  W. T. Thomson,et al.  Current signature analysis to detect induction motor faults , 2001 .

[17]  T. G. Habetler,et al.  Continual on-line training of neural networks with applications to electric machine fault diagnostics , 2001, 2001 IEEE 32nd Annual Power Electronics Specialists Conference (IEEE Cat. No.01CH37230).

[18]  A.J.M. Cardoso,et al.  Bearing failures diagnosis in three-phase induction motors by extended Park's vector approach , 2005, 31st Annual Conference of IEEE Industrial Electronics Society, 2005. IECON 2005..

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

[20]  C.J. Dister,et al.  Using temperature, voltage, and/or speed measurements to improve trending of induction motor RMS currents in process control and diagnostics , 1998, Conference Record of 1998 IEEE Industry Applications Conference. Thirty-Third IAS Annual Meeting (Cat. No.98CH36242).

[21]  Javad Poshtan,et al.  An advanced Park's vectors approach for bearing fault detection , 2009 .

[22]  W. T. Thomson,et al.  Vibration and current monitoring for detecting airgap eccentricity in large induction motors , 1986 .