Application of Self-Organizing Neural Networks to Electrical Fault Classification in Induction Motors

Applied Sciences

Teresa Orlowska-Kowalska¹  Czeslaw T. Kowalski¹  Marcin Wolkiewicz¹  Maciej Skowron¹ 

[1] Department of Electrical Machines, Drives and Measurements, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland;

关键词: induction motor drive;    stator inter-turn short circuits;    rotor bar failures;    fault detection;    current spectra;    SCA analysis;    Kohonen neural networks;   

DOI  :  10.3390/app9040616

来源: DOAJ

【 摘 要 】

Electrical winding faults, namely stator short-circuits and rotor bar damage, in total constitute around 50% of all faults of induction motors (IMs) applied in variable speed drives (VSD). In particular, the short circuits of stator windings are recognized as one of the most difficult failures to detect because their detection makes sense only at the initial stage of the damage. Well-known symptoms of stator and rotor winding failures can be visible in the stator current spectra; however, the detection and classification of motor windings faults usually require the knowledge of human experts. Nowadays, artificial intelligence methods are also used in fault recognition. This paper presents the results of experimental research on the application of the stator current symptoms of the converter-fed induction motor drive to electrical fault detection and classification using Kohonen neural networks. The experimental tests of a diagnostic setup based on a virtual measurement and data pre-processing system, designed in LabView, are described. It has been shown that the developed neural detectors and classifiers based on self-organizing Kohonen maps, trained with the instantaneous symmetrical components of the stator current spectra (ISCA), enable automatic distinguishing between the stator and rotor winding faults for supplying various voltage frequencies and load torque values.

【 授权许可】

Unknown