Numerical magnetic field analysis and signal processing for fault diagnostics of electrical machines

Numerical magnetic field analysis is used for predicting the performance of an induction motor and a slip‐ring generator having different faults implemented in their structure. Virtual measurement data provided by the numerical magnetic field analysis are analysed using modern signal processing techniques to get a reliable indication of the fault. Support vector machine based classification is applied to fault diagnostics. The stator line current, circulating currents between parallel stator branches and forces between the stator and rotor are compared as media of fault detection.

[1]  Robert E. Uhrig,et al.  Monitoring and diagnosis of rolling element bearings using artificial neural networks , 1993, IEEE Trans. Ind. Electron..

[2]  F. Filippetti,et al.  Neural networks aided on-line diagnostics of induction motor rotor faults , 1993 .

[3]  F. Filippetti,et al.  Neural networks aided on-line diagnostics of induction motor rotor faults , 1993, Conference Record of the 1993 IEEE Industry Applications Conference Twenty-Eighth IAS Annual Meeting.

[4]  Ethem Alpaydin,et al.  Support Vector Machines for Multi-class Classification , 1999, IWANN.

[5]  J. L. Coulomb,et al.  A methodology for the determination of global electromechanical quantities from a finite element analysis and its application to the evaluation of magnetic forces, torques and stiffness , 1983 .

[6]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[7]  Mo-Yuen Chow,et al.  Neural-network-based motor rolling bearing fault diagnosis , 2000, IEEE Trans. Ind. Electron..

[8]  Seppo J. Ovaska,et al.  Polynomial predictive filtering in control instrumentation: a review , 1999, IEEE Trans. Ind. Electron..

[9]  Antero Arkkio,et al.  Analysis of induction motors based on the numerical solution of the magnetic field and circuit equations , 1987 .

[10]  S. Poyhonen,et al.  Support vector classification for fault diagnostics of an electrical machine , 2002, 6th International Conference on Signal Processing, 2002..

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

[12]  Ethem Alpaydin,et al.  Support Vector Machine for Multiclass Classification , 1998 .

[13]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .