Diagnosis by pattern recognition for PMSM used in more electric aircraft

Presently, condition monitoring and fault diagnostics in electric drives are essential to optimize maintenance operations and increase reliability levels. This paper presents a diagnosis method for electrical and mechanical faults detection. This method combines a detection method based on expertise with a pattern recognition approach so as to detect different faults appearing on the system but also to classify their origins and their severity by reference to an initial data base. In order to prove reliability and efficiency of this method, experimental results are presented using a permanent magnet synchronous motor (PMSM) drive.

[1]  Tommy W. S. Chow,et al.  Induction machine fault diagnostic analysis with wavelet technique , 2004, IEEE Transactions on Industrial Electronics.

[2]  Sankar K. Pal,et al.  Fuzzy models for pattern recognition : methods that search for structures in data , 1992 .

[3]  David Bonacci,et al.  On-Line Monitoring of Mechanical Faults in Variable-Speed Induction Motor Drives Using the Wigner Distribution , 2008, IEEE Transactions on Industrial Electronics.

[4]  B. Nahid-Mobarakeh,et al.  Back-EMF Based Detection of Stator Winding Inter-turn Fault for PM Synchronous Motor Drives , 2007, 2007 IEEE Vehicle Power and Propulsion Conference.

[5]  Mo-Yuen Chow,et al.  Multiple Discriminant Analysis and Neural-Network-Based Monolith and Partition Fault-Detection Schemes for Broken Rotor Bar in Induction Motors , 2006, IEEE Transactions on Industrial Electronics.

[6]  Tommy W. S. Chow,et al.  Intelligent machine fault detection using SOM based RBF neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[7]  Mineichi Kudo,et al.  Comparison of algorithms that select features for pattern classifiers , 2000, Pattern Recognit..

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

[9]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[10]  Tommy W. S. Chow,et al.  Induction machine fault detection using SOM-based RBF neural networks , 2004, IEEE Transactions on Industrial Electronics.

[11]  E. Boutleux,et al.  Broken bars detection in an induction motor by pattern recognition , 2003, 2003 IEEE Bologna Power Tech Conference Proceedings,.

[12]  Bertrand Raison,et al.  Models for bearing damage detection in induction motors using stator current monitoring , 2008, 2004 IEEE International Symposium on Industrial Electronics.

[13]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[14]  Olivier Ondel,et al.  Diagnostic par reconnaissance des formes : application à un ensemble convertisseur - machine asynchrone. (Diagnosis by Pattern Recognition: application on a set inverter - induction machine) , 2006 .

[15]  Guy Clerc,et al.  Application of pattern recognition method to the diagnosis in induction machine , 2006 .

[16]  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).

[17]  Hong Huo,et al.  A generic neurofuzzy model-based approach for detecting faults in induction motors , 2005, IEEE Transactions on Industrial Electronics.

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

[19]  Sankar K. Pal,et al.  Fuzzy tools for the management of uncertainty in pattern recognition, image analysis, vision and expert systems , 1991 .

[20]  R. M. Tallam,et al.  A survey of methods for detection of stator related faults in induction machines , 2003, 4th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, 2003. SDEMPED 2003..