Diagnostics of Traction Electric Motors of Electric Rolling Stock Using Artificial Neural Networks
Abstract The results of research in the field of diagnostics of a 1DT.003.11 traction electric motor of the ac electric train of the EP3D series by means of modeling of emergency modes of functioning of the kernel neural network are presented. Based on an analysis of the power pseudospectrum graph p...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kosmodamiansky, A. S. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Anmerkung: |
© Allerton Press, Inc. 2022. ISSN 1068-3712, Russian Electrical Engineering, 2022, Vol. 93, No. 9, pp. 576–583. © Allerton Press, Inc., 2022. Russian Text © The Author(s), 2022, published in Elektrotekhnika, 2022, No. 9, pp. 26–34. |
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| Übergeordnetes Werk: |
Enthalten in: Russian electrical engineering - New York, NY : Allerton, 2007, 93(2022), 9 vom: Sept., Seite 576-583 |
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| Übergeordnetes Werk: |
volume:93 ; year:2022 ; number:9 ; month:09 ; pages:576-583 |
| Links: |
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| DOI / URN: |
10.3103/S1068371222090097 |
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| Katalog-ID: |
SPR051215063 |
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| 245 | 1 | 0 | |a Diagnostics of Traction Electric Motors of Electric Rolling Stock Using Artificial Neural Networks |
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| 500 | |a © Allerton Press, Inc. 2022. ISSN 1068-3712, Russian Electrical Engineering, 2022, Vol. 93, No. 9, pp. 576–583. © Allerton Press, Inc., 2022. Russian Text © The Author(s), 2022, published in Elektrotekhnika, 2022, No. 9, pp. 26–34. | ||
| 520 | |a Abstract The results of research in the field of diagnostics of a 1DT.003.11 traction electric motor of the ac electric train of the EP3D series by means of modeling of emergency modes of functioning of the kernel neural network are presented. Based on an analysis of the power pseudospectrum graph performed using digital processing of the current curve waveform in the MATLAB program package, discrete values of the frequency components of the logarithmic current spectrum in the armature winding and their corresponding signal power values are obtained in the form of a table, serving for the failures clustering of the traction electric motor of an electric train. The reliability of traction motors with an increased mileage is proposed to be evaluated using a kernel neural network in the MATLAB package. The result of the study of the diagnostic process using clustering of traction motor failures is an assessment of the effectiveness of the practical use of an artificial neural network with a kernel structure in diagnostic tasks, depending on the technical condition of the traction motor at the current moment of operation. | ||
| 700 | 1 | |a Inkov, Yu. M. |4 aut | |
| 700 | 1 | |a Menshchikov, I. A. |4 aut | |
| 700 | 1 | |a Batashov, S. I. |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Russian electrical engineering |d New York, NY : Allerton, 2007 |g 93(2022), 9 vom: Sept., Seite 576-583 |w (DE-627)531200272 |w (DE-600)2322646-8 |x 1934-8010 |7 nnns |
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10.3103/S1068371222090097 doi (DE-627)SPR051215063 (SPR)S1068371222090097-e DE-627 ger DE-627 rakwb eng Kosmodamiansky, A. S. verfasserin aut Diagnostics of Traction Electric Motors of Electric Rolling Stock Using Artificial Neural Networks 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Allerton Press, Inc. 2022. ISSN 1068-3712, Russian Electrical Engineering, 2022, Vol. 93, No. 9, pp. 576–583. © Allerton Press, Inc., 2022. Russian Text © The Author(s), 2022, published in Elektrotekhnika, 2022, No. 9, pp. 26–34. Abstract The results of research in the field of diagnostics of a 1DT.003.11 traction electric motor of the ac electric train of the EP3D series by means of modeling of emergency modes of functioning of the kernel neural network are presented. Based on an analysis of the power pseudospectrum graph performed using digital processing of the current curve waveform in the MATLAB program package, discrete values of the frequency components of the logarithmic current spectrum in the armature winding and their corresponding signal power values are obtained in the form of a table, serving for the failures clustering of the traction electric motor of an electric train. The reliability of traction motors with an increased mileage is proposed to be evaluated using a kernel neural network in the MATLAB package. The result of the study of the diagnostic process using clustering of traction motor failures is an assessment of the effectiveness of the practical use of an artificial neural network with a kernel structure in diagnostic tasks, depending on the technical condition of the traction motor at the current moment of operation. Inkov, Yu. M. aut Menshchikov, I. A. aut Batashov, S. I. aut Enthalten in Russian electrical engineering New York, NY : Allerton, 2007 93(2022), 9 vom: Sept., Seite 576-583 (DE-627)531200272 (DE-600)2322646-8 1934-8010 nnns volume:93 year:2022 number:9 month:09 pages:576-583 https://dx.doi.org/10.3103/S1068371222090097 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 93 2022 9 09 576-583 |
| spelling |
10.3103/S1068371222090097 doi (DE-627)SPR051215063 (SPR)S1068371222090097-e DE-627 ger DE-627 rakwb eng Kosmodamiansky, A. S. verfasserin aut Diagnostics of Traction Electric Motors of Electric Rolling Stock Using Artificial Neural Networks 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Allerton Press, Inc. 2022. ISSN 1068-3712, Russian Electrical Engineering, 2022, Vol. 93, No. 9, pp. 576–583. © Allerton Press, Inc., 2022. Russian Text © The Author(s), 2022, published in Elektrotekhnika, 2022, No. 9, pp. 26–34. Abstract The results of research in the field of diagnostics of a 1DT.003.11 traction electric motor of the ac electric train of the EP3D series by means of modeling of emergency modes of functioning of the kernel neural network are presented. Based on an analysis of the power pseudospectrum graph performed using digital processing of the current curve waveform in the MATLAB program package, discrete values of the frequency components of the logarithmic current spectrum in the armature winding and their corresponding signal power values are obtained in the form of a table, serving for the failures clustering of the traction electric motor of an electric train. The reliability of traction motors with an increased mileage is proposed to be evaluated using a kernel neural network in the MATLAB package. The result of the study of the diagnostic process using clustering of traction motor failures is an assessment of the effectiveness of the practical use of an artificial neural network with a kernel structure in diagnostic tasks, depending on the technical condition of the traction motor at the current moment of operation. Inkov, Yu. M. aut Menshchikov, I. A. aut Batashov, S. I. aut Enthalten in Russian electrical engineering New York, NY : Allerton, 2007 93(2022), 9 vom: Sept., Seite 576-583 (DE-627)531200272 (DE-600)2322646-8 1934-8010 nnns volume:93 year:2022 number:9 month:09 pages:576-583 https://dx.doi.org/10.3103/S1068371222090097 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 93 2022 9 09 576-583 |
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10.3103/S1068371222090097 doi (DE-627)SPR051215063 (SPR)S1068371222090097-e DE-627 ger DE-627 rakwb eng Kosmodamiansky, A. S. verfasserin aut Diagnostics of Traction Electric Motors of Electric Rolling Stock Using Artificial Neural Networks 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Allerton Press, Inc. 2022. ISSN 1068-3712, Russian Electrical Engineering, 2022, Vol. 93, No. 9, pp. 576–583. © Allerton Press, Inc., 2022. Russian Text © The Author(s), 2022, published in Elektrotekhnika, 2022, No. 9, pp. 26–34. Abstract The results of research in the field of diagnostics of a 1DT.003.11 traction electric motor of the ac electric train of the EP3D series by means of modeling of emergency modes of functioning of the kernel neural network are presented. Based on an analysis of the power pseudospectrum graph performed using digital processing of the current curve waveform in the MATLAB program package, discrete values of the frequency components of the logarithmic current spectrum in the armature winding and their corresponding signal power values are obtained in the form of a table, serving for the failures clustering of the traction electric motor of an electric train. The reliability of traction motors with an increased mileage is proposed to be evaluated using a kernel neural network in the MATLAB package. The result of the study of the diagnostic process using clustering of traction motor failures is an assessment of the effectiveness of the practical use of an artificial neural network with a kernel structure in diagnostic tasks, depending on the technical condition of the traction motor at the current moment of operation. Inkov, Yu. M. aut Menshchikov, I. A. aut Batashov, S. I. aut Enthalten in Russian electrical engineering New York, NY : Allerton, 2007 93(2022), 9 vom: Sept., Seite 576-583 (DE-627)531200272 (DE-600)2322646-8 1934-8010 nnns volume:93 year:2022 number:9 month:09 pages:576-583 https://dx.doi.org/10.3103/S1068371222090097 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 93 2022 9 09 576-583 |
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10.3103/S1068371222090097 doi (DE-627)SPR051215063 (SPR)S1068371222090097-e DE-627 ger DE-627 rakwb eng Kosmodamiansky, A. S. verfasserin aut Diagnostics of Traction Electric Motors of Electric Rolling Stock Using Artificial Neural Networks 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Allerton Press, Inc. 2022. ISSN 1068-3712, Russian Electrical Engineering, 2022, Vol. 93, No. 9, pp. 576–583. © Allerton Press, Inc., 2022. Russian Text © The Author(s), 2022, published in Elektrotekhnika, 2022, No. 9, pp. 26–34. Abstract The results of research in the field of diagnostics of a 1DT.003.11 traction electric motor of the ac electric train of the EP3D series by means of modeling of emergency modes of functioning of the kernel neural network are presented. Based on an analysis of the power pseudospectrum graph performed using digital processing of the current curve waveform in the MATLAB program package, discrete values of the frequency components of the logarithmic current spectrum in the armature winding and their corresponding signal power values are obtained in the form of a table, serving for the failures clustering of the traction electric motor of an electric train. The reliability of traction motors with an increased mileage is proposed to be evaluated using a kernel neural network in the MATLAB package. The result of the study of the diagnostic process using clustering of traction motor failures is an assessment of the effectiveness of the practical use of an artificial neural network with a kernel structure in diagnostic tasks, depending on the technical condition of the traction motor at the current moment of operation. Inkov, Yu. M. aut Menshchikov, I. A. aut Batashov, S. I. aut Enthalten in Russian electrical engineering New York, NY : Allerton, 2007 93(2022), 9 vom: Sept., Seite 576-583 (DE-627)531200272 (DE-600)2322646-8 1934-8010 nnns volume:93 year:2022 number:9 month:09 pages:576-583 https://dx.doi.org/10.3103/S1068371222090097 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 93 2022 9 09 576-583 |
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10.3103/S1068371222090097 doi (DE-627)SPR051215063 (SPR)S1068371222090097-e DE-627 ger DE-627 rakwb eng Kosmodamiansky, A. S. verfasserin aut Diagnostics of Traction Electric Motors of Electric Rolling Stock Using Artificial Neural Networks 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Allerton Press, Inc. 2022. ISSN 1068-3712, Russian Electrical Engineering, 2022, Vol. 93, No. 9, pp. 576–583. © Allerton Press, Inc., 2022. Russian Text © The Author(s), 2022, published in Elektrotekhnika, 2022, No. 9, pp. 26–34. Abstract The results of research in the field of diagnostics of a 1DT.003.11 traction electric motor of the ac electric train of the EP3D series by means of modeling of emergency modes of functioning of the kernel neural network are presented. Based on an analysis of the power pseudospectrum graph performed using digital processing of the current curve waveform in the MATLAB program package, discrete values of the frequency components of the logarithmic current spectrum in the armature winding and their corresponding signal power values are obtained in the form of a table, serving for the failures clustering of the traction electric motor of an electric train. The reliability of traction motors with an increased mileage is proposed to be evaluated using a kernel neural network in the MATLAB package. The result of the study of the diagnostic process using clustering of traction motor failures is an assessment of the effectiveness of the practical use of an artificial neural network with a kernel structure in diagnostic tasks, depending on the technical condition of the traction motor at the current moment of operation. Inkov, Yu. M. aut Menshchikov, I. A. aut Batashov, S. I. aut Enthalten in Russian electrical engineering New York, NY : Allerton, 2007 93(2022), 9 vom: Sept., Seite 576-583 (DE-627)531200272 (DE-600)2322646-8 1934-8010 nnns volume:93 year:2022 number:9 month:09 pages:576-583 https://dx.doi.org/10.3103/S1068371222090097 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 93 2022 9 09 576-583 |
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Kosmodamiansky, A. S. Diagnostics of Traction Electric Motors of Electric Rolling Stock Using Artificial Neural Networks |
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Diagnostics of Traction Electric Motors of Electric Rolling Stock Using Artificial Neural Networks |
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diagnostics of traction electric motors of electric rolling stock using artificial neural networks |
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Diagnostics of Traction Electric Motors of Electric Rolling Stock Using Artificial Neural Networks |
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Abstract The results of research in the field of diagnostics of a 1DT.003.11 traction electric motor of the ac electric train of the EP3D series by means of modeling of emergency modes of functioning of the kernel neural network are presented. Based on an analysis of the power pseudospectrum graph performed using digital processing of the current curve waveform in the MATLAB program package, discrete values of the frequency components of the logarithmic current spectrum in the armature winding and their corresponding signal power values are obtained in the form of a table, serving for the failures clustering of the traction electric motor of an electric train. The reliability of traction motors with an increased mileage is proposed to be evaluated using a kernel neural network in the MATLAB package. The result of the study of the diagnostic process using clustering of traction motor failures is an assessment of the effectiveness of the practical use of an artificial neural network with a kernel structure in diagnostic tasks, depending on the technical condition of the traction motor at the current moment of operation. © Allerton Press, Inc. 2022. ISSN 1068-3712, Russian Electrical Engineering, 2022, Vol. 93, No. 9, pp. 576–583. © Allerton Press, Inc., 2022. Russian Text © The Author(s), 2022, published in Elektrotekhnika, 2022, No. 9, pp. 26–34. |
| abstractGer |
Abstract The results of research in the field of diagnostics of a 1DT.003.11 traction electric motor of the ac electric train of the EP3D series by means of modeling of emergency modes of functioning of the kernel neural network are presented. Based on an analysis of the power pseudospectrum graph performed using digital processing of the current curve waveform in the MATLAB program package, discrete values of the frequency components of the logarithmic current spectrum in the armature winding and their corresponding signal power values are obtained in the form of a table, serving for the failures clustering of the traction electric motor of an electric train. The reliability of traction motors with an increased mileage is proposed to be evaluated using a kernel neural network in the MATLAB package. The result of the study of the diagnostic process using clustering of traction motor failures is an assessment of the effectiveness of the practical use of an artificial neural network with a kernel structure in diagnostic tasks, depending on the technical condition of the traction motor at the current moment of operation. © Allerton Press, Inc. 2022. ISSN 1068-3712, Russian Electrical Engineering, 2022, Vol. 93, No. 9, pp. 576–583. © Allerton Press, Inc., 2022. Russian Text © The Author(s), 2022, published in Elektrotekhnika, 2022, No. 9, pp. 26–34. |
| abstract_unstemmed |
Abstract The results of research in the field of diagnostics of a 1DT.003.11 traction electric motor of the ac electric train of the EP3D series by means of modeling of emergency modes of functioning of the kernel neural network are presented. Based on an analysis of the power pseudospectrum graph performed using digital processing of the current curve waveform in the MATLAB program package, discrete values of the frequency components of the logarithmic current spectrum in the armature winding and their corresponding signal power values are obtained in the form of a table, serving for the failures clustering of the traction electric motor of an electric train. The reliability of traction motors with an increased mileage is proposed to be evaluated using a kernel neural network in the MATLAB package. The result of the study of the diagnostic process using clustering of traction motor failures is an assessment of the effectiveness of the practical use of an artificial neural network with a kernel structure in diagnostic tasks, depending on the technical condition of the traction motor at the current moment of operation. © Allerton Press, Inc. 2022. ISSN 1068-3712, Russian Electrical Engineering, 2022, Vol. 93, No. 9, pp. 576–583. © Allerton Press, Inc., 2022. Russian Text © The Author(s), 2022, published in Elektrotekhnika, 2022, No. 9, pp. 26–34. |
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Diagnostics of Traction Electric Motors of Electric Rolling Stock Using Artificial Neural Networks |
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S.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Diagnostics of Traction Electric Motors of Electric Rolling Stock Using Artificial Neural Networks</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Allerton Press, Inc. 2022. ISSN 1068-3712, Russian Electrical Engineering, 2022, Vol. 93, No. 9, pp. 576–583. © Allerton Press, Inc., 2022. Russian Text © The Author(s), 2022, published in Elektrotekhnika, 2022, No. 9, pp. 26–34.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The results of research in the field of diagnostics of a 1DT.003.11 traction electric motor of the ac electric train of the EP3D series by means of modeling of emergency modes of functioning of the kernel neural network are presented. Based on an analysis of the power pseudospectrum graph performed using digital processing of the current curve waveform in the MATLAB program package, discrete values of the frequency components of the logarithmic current spectrum in the armature winding and their corresponding signal power values are obtained in the form of a table, serving for the failures clustering of the traction electric motor of an electric train. 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