Detection of inter-turn short-circuit at start-up of induction machine based on torque analysis
Recently, interest in new diagnostics methods in a field of induction machines was observed. Research presented in the paper shows the diagnostics of induction machine based on torque pulsation, under inter-turn short-circuit, during start-up of a machine. In the paper three numerical techniques wer...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2017-12-01
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| Series: | Open Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/phys-2017-0101 |
| _version_ | 1818669054215323648 |
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| author | Pietrowski Wojciech Górny Konrad |
| author_facet | Pietrowski Wojciech Górny Konrad |
| author_sort | Pietrowski Wojciech |
| collection | DOAJ |
| description | Recently, interest in new diagnostics methods in a field of induction machines was observed. Research presented in the paper shows the diagnostics of induction machine based on torque pulsation, under inter-turn short-circuit, during start-up of a machine. In the paper three numerical techniques were used: finite element analysis, signal analysis and artificial neural networks (ANN). The elaborated numerical model of faulty machine consists of field, circuit and motion equations. Voltage excited supply allowed to determine the torque waveform during start-up. The inter-turn short-circuit was treated as a galvanic connection between two points of the stator winding. The waveforms were calculated for different amounts of shorted-turns from 0 to 55. Due to the non-stationary waveforms a wavelet packet decomposition was used to perform an analysis of the torque. The obtained results of analysis were used as input vector for ANN. The response of the neural network was the number of shorted-turns in the stator winding. Special attention was paid to compare response of general regression neural network (GRNN) and multi-layer perceptron neural network (MLP). Based on the results of the research, the efficiency of the developed algorithm can be inferred. |
| first_indexed | 2024-12-17T06:46:07Z |
| format | Article |
| id | doaj.art-998bf40ff7f44232a0e315127aacffa4 |
| institution | Directory Open Access Journal |
| issn | 2391-5471 |
| language | English |
| last_indexed | 2024-12-17T06:46:07Z |
| publishDate | 2017-12-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Open Physics |
| spelling | doaj.art-998bf40ff7f44232a0e315127aacffa42022-12-21T21:59:44ZengDe GruyterOpen Physics2391-54712017-12-0115185185610.1515/phys-2017-0101phys-2017-0101Detection of inter-turn short-circuit at start-up of induction machine based on torque analysisPietrowski Wojciech0Górny Konrad1Institute of Electrical Engineering and Electronics, Poznan University of Technology, 60-965Poznan, PolandInstitute of Electrical Engineering and Electronics, Poznan University of Technology, 60-965Poznan, PolandRecently, interest in new diagnostics methods in a field of induction machines was observed. Research presented in the paper shows the diagnostics of induction machine based on torque pulsation, under inter-turn short-circuit, during start-up of a machine. In the paper three numerical techniques were used: finite element analysis, signal analysis and artificial neural networks (ANN). The elaborated numerical model of faulty machine consists of field, circuit and motion equations. Voltage excited supply allowed to determine the torque waveform during start-up. The inter-turn short-circuit was treated as a galvanic connection between two points of the stator winding. The waveforms were calculated for different amounts of shorted-turns from 0 to 55. Due to the non-stationary waveforms a wavelet packet decomposition was used to perform an analysis of the torque. The obtained results of analysis were used as input vector for ANN. The response of the neural network was the number of shorted-turns in the stator winding. Special attention was paid to compare response of general regression neural network (GRNN) and multi-layer perceptron neural network (MLP). Based on the results of the research, the efficiency of the developed algorithm can be inferred.https://doi.org/10.1515/phys-2017-0101wavelet transforminduction machinefault analysisfinite element methodartificial neural networks85.80.jm |
| spellingShingle | Pietrowski Wojciech Górny Konrad Detection of inter-turn short-circuit at start-up of induction machine based on torque analysis Open Physics wavelet transform induction machine fault analysis finite element method artificial neural networks 85.80.jm |
| title | Detection of inter-turn short-circuit at start-up of induction machine based on torque analysis |
| title_full | Detection of inter-turn short-circuit at start-up of induction machine based on torque analysis |
| title_fullStr | Detection of inter-turn short-circuit at start-up of induction machine based on torque analysis |
| title_full_unstemmed | Detection of inter-turn short-circuit at start-up of induction machine based on torque analysis |
| title_short | Detection of inter-turn short-circuit at start-up of induction machine based on torque analysis |
| title_sort | detection of inter turn short circuit at start up of induction machine based on torque analysis |
| topic | wavelet transform induction machine fault analysis finite element method artificial neural networks 85.80.jm |
| url | https://doi.org/10.1515/phys-2017-0101 |
| work_keys_str_mv | AT pietrowskiwojciech detectionofinterturnshortcircuitatstartupofinductionmachinebasedontorqueanalysis AT gornykonrad detectionofinterturnshortcircuitatstartupofinductionmachinebasedontorqueanalysis |