Effectiveness of Selected Neural Network Structures Based on Axial Flux Analysis in Stator and Rotor Winding Incipient Fault Detection of Inverter-fed Induction Motors
This paper presents a comparative study on the application of different neural network structures to early detection of electrical faults in induction motor drives. The diagnosis inference of the stator inter-turn short-circuits and broken rotor bars is based on the analysis of an axial flux of the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-06-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/12/12/2392 |
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| author | Maciej Skowron Marcin Wolkiewicz Teresa Orlowska-Kowalska Czeslaw T. Kowalski |
| author_facet | Maciej Skowron Marcin Wolkiewicz Teresa Orlowska-Kowalska Czeslaw T. Kowalski |
| author_sort | Maciej Skowron |
| collection | DOAJ |
| description | This paper presents a comparative study on the application of different neural network structures to early detection of electrical faults in induction motor drives. The diagnosis inference of the stator inter-turn short-circuits and broken rotor bars is based on the analysis of an axial flux of the induction motor. In order to automate the fault detection process, three different structures of neural networks were used: multi-layer perceptron, self-organizing Kohonen network and recursive Hopfield network. Tests were carried out for various levels of stator and rotor failures. In order to assess the sensitivity of the applied neural detectors, the tests were carried out for variable load conditions and for different values of the supply voltage frequency. Experimental results of the elaborated neural detectors are presented and discussed. |
| first_indexed | 2024-04-12T19:32:12Z |
| format | Article |
| id | doaj.art-a50c203a8a0e46589de9bcaa74ce2b5a |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-12T19:32:12Z |
| publishDate | 2019-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-a50c203a8a0e46589de9bcaa74ce2b5a2022-12-22T03:19:18ZengMDPI AGEnergies1996-10732019-06-011212239210.3390/en12122392en12122392Effectiveness of Selected Neural Network Structures Based on Axial Flux Analysis in Stator and Rotor Winding Incipient Fault Detection of Inverter-fed Induction MotorsMaciej Skowron0Marcin Wolkiewicz1Teresa Orlowska-Kowalska2Czeslaw T. Kowalski3Department of Electrical Machines, Drives and Measurements, Wroclaw University of Science and Technology, 50-370 Wroclaw, PolandDepartment of Electrical Machines, Drives and Measurements, Wroclaw University of Science and Technology, 50-370 Wroclaw, PolandDepartment of Electrical Machines, Drives and Measurements, Wroclaw University of Science and Technology, 50-370 Wroclaw, PolandDepartment of Electrical Machines, Drives and Measurements, Wroclaw University of Science and Technology, 50-370 Wroclaw, PolandThis paper presents a comparative study on the application of different neural network structures to early detection of electrical faults in induction motor drives. The diagnosis inference of the stator inter-turn short-circuits and broken rotor bars is based on the analysis of an axial flux of the induction motor. In order to automate the fault detection process, three different structures of neural networks were used: multi-layer perceptron, self-organizing Kohonen network and recursive Hopfield network. Tests were carried out for various levels of stator and rotor failures. In order to assess the sensitivity of the applied neural detectors, the tests were carried out for variable load conditions and for different values of the supply voltage frequency. Experimental results of the elaborated neural detectors are presented and discussed.https://www.mdpi.com/1996-1073/12/12/2392induction motor drivestator faultrotor faultaxial fluxneural networksfault detectionMLP networkKohonen networkHopfield recursive network |
| spellingShingle | Maciej Skowron Marcin Wolkiewicz Teresa Orlowska-Kowalska Czeslaw T. Kowalski Effectiveness of Selected Neural Network Structures Based on Axial Flux Analysis in Stator and Rotor Winding Incipient Fault Detection of Inverter-fed Induction Motors Energies induction motor drive stator fault rotor fault axial flux neural networks fault detection MLP network Kohonen network Hopfield recursive network |
| title | Effectiveness of Selected Neural Network Structures Based on Axial Flux Analysis in Stator and Rotor Winding Incipient Fault Detection of Inverter-fed Induction Motors |
| title_full | Effectiveness of Selected Neural Network Structures Based on Axial Flux Analysis in Stator and Rotor Winding Incipient Fault Detection of Inverter-fed Induction Motors |
| title_fullStr | Effectiveness of Selected Neural Network Structures Based on Axial Flux Analysis in Stator and Rotor Winding Incipient Fault Detection of Inverter-fed Induction Motors |
| title_full_unstemmed | Effectiveness of Selected Neural Network Structures Based on Axial Flux Analysis in Stator and Rotor Winding Incipient Fault Detection of Inverter-fed Induction Motors |
| title_short | Effectiveness of Selected Neural Network Structures Based on Axial Flux Analysis in Stator and Rotor Winding Incipient Fault Detection of Inverter-fed Induction Motors |
| title_sort | effectiveness of selected neural network structures based on axial flux analysis in stator and rotor winding incipient fault detection of inverter fed induction motors |
| topic | induction motor drive stator fault rotor fault axial flux neural networks fault detection MLP network Kohonen network Hopfield recursive network |
| url | https://www.mdpi.com/1996-1073/12/12/2392 |
| work_keys_str_mv | AT maciejskowron effectivenessofselectedneuralnetworkstructuresbasedonaxialfluxanalysisinstatorandrotorwindingincipientfaultdetectionofinverterfedinductionmotors AT marcinwolkiewicz effectivenessofselectedneuralnetworkstructuresbasedonaxialfluxanalysisinstatorandrotorwindingincipientfaultdetectionofinverterfedinductionmotors AT teresaorlowskakowalska effectivenessofselectedneuralnetworkstructuresbasedonaxialfluxanalysisinstatorandrotorwindingincipientfaultdetectionofinverterfedinductionmotors AT czeslawtkowalski effectivenessofselectedneuralnetworkstructuresbasedonaxialfluxanalysisinstatorandrotorwindingincipientfaultdetectionofinverterfedinductionmotors |