Abstract
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.
Highlights
Nowadays variable speed drives (VSD) with induction motors (IM) are widely used in many industrial processes and plants
After the introduction with a brief overview of the technical literature dedicated to fault detection of IM stator and rotor windings, the method of instantaneous symmetrical components analysis (ISCA) is presented in Section 2, as it was used in this research work for the analysis of the electrical winding damage of the induction motor drive
The characteristic frequency components, that characterize faults in the stator winding (ITSCs) maps were implemented in the LabVIEW environment of National Instruments using Matlab software are known as [14]:
Summary
Nowadays variable speed drives (VSD) with induction motors (IM) are widely used in many industrial processes and plants. According to the best knowledge of the authors, the Kohonen self-organizing networks were not applied to the detection of incipient stator faults as well as for the multiple electrical faults classification of the induction motor windings, namely stator short-circuit turns and rotor bar faults simultaneously. 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 (SCA), enable the automatic distinguishing between the stator and rotor winding faults for various supplying voltage frequencies and load torque values. After the introduction with a brief overview of the technical literature dedicated to fault detection of IM stator and rotor windings, the method of instantaneous symmetrical components analysis (ISCA) is presented, as it was used in this research work for the analysis of the electrical winding damage of the induction motor drive.
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