Enhancing the Efficiency of Failure Recognition in Induction Machines through the Application of Deep Neural Networks
The objective of the investigation was to increase the effectiveness of damage detection in the stator of the squirrel-cage induction machine. The analysis aimed to enhance the operational trustworthiness of the squirrel-cage induction machine by employing nonintrusive diagnostic methods based on a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/2/476 |
| _version_ | 1797344154120880128 |
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| author | Wojciech Pietrowski Konrad Górny |
| author_facet | Wojciech Pietrowski Konrad Górny |
| author_sort | Wojciech Pietrowski |
| collection | DOAJ |
| description | The objective of the investigation was to increase the effectiveness of damage detection in the stator of the squirrel-cage induction machine. The analysis aimed to enhance the operational trustworthiness of the squirrel-cage induction machine by employing nonintrusive diagnostic methods based on a current signal and modern artificial intelligence methods. The authors of the study introduced a diagnostic technique for identifying multiphase interturn short circuits of stator winding. These short circuits are one of the most common faults in induction machines. The proposed method focusses on deriving a diagnostic signal from the phase-current waveforms of the machine. The noninvasive nature of the diagnostic technique presented is attributed to the application of the field model of electromagnetic phenomena to determine the diagnostic signal. For this purpose, a field model of a squirrel-cage machine was developed. The waveforms of phase currents obtained from the field model were used as input into an elaborated machine failure neural classifier. A deep neural network was used to develop a neural classifier. The effectiveness of the developed classifier has been experimentally verified, and the obtained results have been presented, concluded, and discussed. The scientific novelty presented in the article is the presentation of research results on the use of a neural classifier to detect damage in all phases of the stator winding at an early stage of its appearance. The features of this type of damage are very difficult to observe in signal waveforms such as a phase current or torque. |
| first_indexed | 2024-03-08T10:58:14Z |
| format | Article |
| id | doaj.art-7700d1f20a7d4f35b5558885f225ddb7 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-08T10:58:14Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-7700d1f20a7d4f35b5558885f225ddb72024-01-26T16:20:38ZengMDPI AGEnergies1996-10732024-01-0117247610.3390/en17020476Enhancing the Efficiency of Failure Recognition in Induction Machines through the Application of Deep Neural NetworksWojciech Pietrowski0Konrad Górny1Institute of Electrical Engineering and Electronics, Faculty of Control, Robotics and Electrical Engineering, Poznan University of Technology, Piotrowo Street, No. 3a, 60-965 Poznan, PolandInstitute of Electrical Engineering and Electronics, Faculty of Control, Robotics and Electrical Engineering, Poznan University of Technology, Piotrowo Street, No. 3a, 60-965 Poznan, PolandThe objective of the investigation was to increase the effectiveness of damage detection in the stator of the squirrel-cage induction machine. The analysis aimed to enhance the operational trustworthiness of the squirrel-cage induction machine by employing nonintrusive diagnostic methods based on a current signal and modern artificial intelligence methods. The authors of the study introduced a diagnostic technique for identifying multiphase interturn short circuits of stator winding. These short circuits are one of the most common faults in induction machines. The proposed method focusses on deriving a diagnostic signal from the phase-current waveforms of the machine. The noninvasive nature of the diagnostic technique presented is attributed to the application of the field model of electromagnetic phenomena to determine the diagnostic signal. For this purpose, a field model of a squirrel-cage machine was developed. The waveforms of phase currents obtained from the field model were used as input into an elaborated machine failure neural classifier. A deep neural network was used to develop a neural classifier. The effectiveness of the developed classifier has been experimentally verified, and the obtained results have been presented, concluded, and discussed. The scientific novelty presented in the article is the presentation of research results on the use of a neural classifier to detect damage in all phases of the stator winding at an early stage of its appearance. The features of this type of damage are very difficult to observe in signal waveforms such as a phase current or torque.https://www.mdpi.com/1996-1073/17/2/476induction machinefinite-element methodanalysis of a current waveformdeep neural networknoninvasive diagnosticsmultiphase interturn short circuits |
| spellingShingle | Wojciech Pietrowski Konrad Górny Enhancing the Efficiency of Failure Recognition in Induction Machines through the Application of Deep Neural Networks Energies induction machine finite-element method analysis of a current waveform deep neural network noninvasive diagnostics multiphase interturn short circuits |
| title | Enhancing the Efficiency of Failure Recognition in Induction Machines through the Application of Deep Neural Networks |
| title_full | Enhancing the Efficiency of Failure Recognition in Induction Machines through the Application of Deep Neural Networks |
| title_fullStr | Enhancing the Efficiency of Failure Recognition in Induction Machines through the Application of Deep Neural Networks |
| title_full_unstemmed | Enhancing the Efficiency of Failure Recognition in Induction Machines through the Application of Deep Neural Networks |
| title_short | Enhancing the Efficiency of Failure Recognition in Induction Machines through the Application of Deep Neural Networks |
| title_sort | enhancing the efficiency of failure recognition in induction machines through the application of deep neural networks |
| topic | induction machine finite-element method analysis of a current waveform deep neural network noninvasive diagnostics multiphase interturn short circuits |
| url | https://www.mdpi.com/1996-1073/17/2/476 |
| work_keys_str_mv | AT wojciechpietrowski enhancingtheefficiencyoffailurerecognitionininductionmachinesthroughtheapplicationofdeepneuralnetworks AT konradgorny enhancingtheefficiencyoffailurerecognitionininductionmachinesthroughtheapplicationofdeepneuralnetworks |