A case study of the use of statistical processing of the armature rotation irregularities for the diagnostics of locomotive traction electric motors
Abstract
Locomotive Traction Electric Motors (TEMs) generate power to rotate the wheelsets of diesel or electric locomotives, electric or diesel multiple units. TEMs are the most critical parts of traction rolling stock on which exploitation costs, reliability and train traffic safety depend. The purpose of the article is to evaluate the possibility of diagnostics the technical condition of the TEM using the rotation irregularities of the armature shaft in the electric motor as a diagnostic parameter. The article analyses the main causes of TEM failures and methods for diagnosing electric motors in operation. The expediency of using the rotation irregularities of the armature shaft in the electric motor as a diagnostic parameter is substantiated. The structural flowchart of the device for measuring the rotation irregularities of the armature is presented. Diagnosing the mechanical part of an electric motor is chosen as an implementation example. The authors confirmed the connection between the technical condition of the electric motor and the statistical indicators calculated for the signal of the rotation irregularities of the armature shaft.
Keyword : locomotive, traction electric motor, defect, diagnostic, rotation irregularity, statistical metric, diagnostic signal
How to Cite
Bodnar, B., Ochkаsov O., Hryshechkina, T., & Petrenko, V. (2025). A case study of the use of statistical processing of the armature rotation irregularities for the diagnostics of locomotive traction electric motors. Transport, 40(1), 24–34. https://doi.org/10.3846/transport.2025.23229
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Al-Janabi, S. T. F.; Saeed, H. A. 2011. A neural network based anomaly intrusion detection system, 2011 Developments in E-Systems Engineering 12543636, 6–8 December 2011, Dubai, United Arab Emirates, 221–226. https://doi.org/10.1109/DeSE.2011.19
Asad, B.; Vaimann, T.; Belahcen, A.; Kallaste, A.; Rassõlkin, A.; Iqbal, M. N. 2020. The cluster computation-based hybrid fem–analytical model of induction motor for fault diagnostics, Applied Sciences 10(21): 7572. https://doi.org/10.3390/app10217572
Barkov, A. V.; Barkova, N. A. 2013. Vibracionnaja diagnostika mashin i oborudovanija. Analiz vibracii. Sankt-Peterburg: Sevzapuchcentr. 152 s. (in Russian).
Basaran, M.; Fidan, M. 2021. Induction motor fault classification via entropy and column correlation features of 2D represented vibration data, Eksploatacja i Niezawodność – Maintenance and Reliability 23(1): 132–142. https://doi.org/10.17531/ein.2021.1.14
Bodnar, B.; Ochkasov, O. 2021. Devising a procedure to form the diagnostic parameters for locomotives using a principal components analysis, Eastern-European Journal of Enterprise Technologies 2(1): 97–103. https://doi.org/10.15587/1729-4061.2021.230293
Bodnar, B.; Ochkasov, O.; Bobyr, D.; Korenyuk, R.; Bazaras, Ž. 2018a. Using the self-braking method when the post-overhaul diagnostics of diesel-hydraulic locomotives, in Transport Means 2018: Proceedings of the 22nd International Scientific Conference, 3–5 October 2018, Trakai, Lithuania, 2: 914–919.
Bodnar, B.; Ochkasov, O.; Chernyaev, D.; Skvireckas, R. 2018b. Use of the wavelet transform for the analysis of irregularity of crankshaft angular velocity, in Transport Means 2018: Proceedings of the 22nd International Scientific Conference, 3–5 October 2018, Trakai, Lithuania, 2: 962–967.
Bodnar, B. Je.; Ochkasov, O. B.; Chernjajev, D. V.; Shevchenko, Ja. I. 2013. Diagnostuvannja tjagovyh elektrodvyguniv za nerivnomirnistju obertannja jakorja, Nauka ta progres transportu 3(45): 13–21. https://doi.org/10.15802/stp2013/14793 (in Ukrainian).
Bodnar, B.; Ochkasov, O.; Ochkasov, M. 2021. Devising a procedure for calculating the technical condition index of locomotive nodes based on monitoring results, Eastern-European Journal of Enterprise Technologies 5(3): 37–45. https://doi.org/10.15587/1729-4061.2021.242478
Dacun, Ju. N. 2015. Vybor strategii tehnicheskogo obsluzhivanija i remonta lokomotivov na osnove metodov nechetkoj logiki, Visnyk Shidnoukrai’ns’kogo nacional’nogo universytetu imeni Volodymyra Dalja 1: 77–80. Available from Internet: http://nbuv.gov.ua/UJRN/VSUNU_2015_1_17 (in Russian).
Evgrafov, A. N.; Karazin, V. I.; Petrov, G. N. 2019. Analysis of the Self-braking Effect of Linkage Mechanisms, in A. N. Evgrafov (Ed.). Advances in Mechanical Engineering: Selected Contributions from the Conference “Modern Engineering: Science and Education”, Saint Petersburg, Russia, May 2018, 119–127. https://doi.org/10.1007/978-3-030-11981-2_11
Garramiola, F.; Poza, J.; Madina, P.; Del Olmo, J.; Almandoz, G. 2018. A review in fault diagnosis and health assessment for railway traction drives, Applied Sciences 8(12): 2475. https://doi.org/10.3390/app8122475
ISO 17359:2018. Condition Monitoring and Diagnostics of Machines. General Guidelines.
Kapitsa, M.; Laguta, V.; Kozik, Y. 2019. Classification of quality conditions of a traction motor frame insulation of locomotives, MATEC Web of Conferences 294: 03002. https://doi.org/10.1051/matecconf/201929403002
Kapitsa, M.; Laguta, V.; Kozik, Y. 2018. Selecting the parameters of the diagnosis of frame insulation condition in electrical machines of locomotives, International Journal of Engineering & Technology 7(4.3): 110–114. https://doi.org/10.14419/ijet.v7i4.3.19718
Kljushnyk, I. A. 2017. Doslidzhennja mozhlyvosti vykorystannja nejronnyh merezh pry vyprobuvannjah gidravlichnyh peredach teplovoziv, Informacijno-kerujuchi systemy na zaliznychnomu transporti (5): 8–15. https://doi.org/10.18664/ikszt.v0i5.115370 (in Ukrainian).
Li, Y.; Lu, A.; Wu, X.; Yuan, S. 2019. Dynamic anomaly detection using vector autoregressive model, Lecture Notes in Computer Science 11439: 600–611. https://doi.org/10.1007/978-3-030-16148-4_46
Lis, A.; Dworakowski, Z.; Czubak, P. 2021. An anomaly detection method for rotating machinery monitoring based on the most representative data, Journal of Vibroengineering 23(4): 861–876. https://doi.org/10.21595/jve.2021.21622
Lu, J.; Qian, W.; Li, S.; Cui, R. 2021. Enhanced k-nearest neighbor for intelligent fault diagnosis of rotating machinery, Applied Sciences 11(3): 919. https://doi.org/10.3390/app11030919
Luk’janov, A. V.; Perelygina, A. Ju.; Chegaev, N. S. 2017. Obrabotka dannyh vibracionnogo kontrolja vspomogatel’nyh mashin jelektrovozov, Sovremennye tehnologii. Sistemnyj analiz. Modelirovanie 3(55): 119–125. (in Russian).
Malla, C.; Panigrahi, I. 2019. Review of condition monitoring of rolling element bearing using vibration analysis and other techniques, Journal of Vibration Engineering & Technologies 7: 407–414. https://doi.org/10.1007/s42417-019-00119-y
Munir, M.; Siddiqui, S. A.; Chattha, M. A.; Dengel, A.; Ahmed, S. 2019. FuseAD: unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models, Sensors 19(11): 2451. https://doi.org/10.3390/s19112451
Pavlenko, T.; Shavkun, V.; Petrenko, A. 2017. Ways to improve operation reliability of traction electric motors of the rolling stock of electric transport, Eastern-European Journal of Enterprise Technologies 5(8): 22–30. https://doi.org/10.15587/1729-4061.2017.112109
Sakaidani, Y.; Kondo, M. 2018. Bearing fault detection for railway traction motors through leakage current, in 2018 XIII International Conference on Electrical Machines (ICEM), 3–6 September 2018, Alexandroupoli, Greece, 1768–1774. https://doi.org/10.1109/ICELMACH.2018.8506796
Saleem, A.; Diwakar, G.; Satyanarayana, M. R. S. 2012. Detection of unbalance in rotating machines using shaft deflection measurement during its operation, IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE) 3(3): 8–20. https://doi.org/10.9790/1684-0330820
Serdjuk, T. M. 2018. Diagnostuvannja tryfaznyh asynhronnyh dvyguniv, Elektromagnitna sumisnist’ ta bezpeka na zaliznychnomu transporti 16: 32–42. Available from Internet: http://ecsrt.diit.edu.ua/article/view/172537
Udovenko, S. G.; Kelembet, D. V.; Teslenko, O. V. 2020. Obrobka informacii’v skladnyh tehnichnyh systemah, Systemy obrobky informacii’ 1(160): 7–17. https://doi.org/10.30748/soi.2020.160.01 (in Ukrainian).
Venkatesan, S.; Manickavasagam, K.; Tengenkai, N.; Vijayalakshmi, N. 2019. Health monitoring and prognosis of electric vehicle motor using intelligent-digital twin, IET Electric Power Applications 13(9): 1328–1335. https://doi.org/10.1049/iet-epa.2018.5732
Zhao, Y.; Liu, P.; Wang, Z.; Zhang, L.; Hong, J. 2017. Fault and defect diagnosis of battery for electric vehicles based on big data analysis methods, Applied Energy 207: 354–362. https://doi.org/10.1016/j.apenergy.2017.05.139
Zhou, Z.; Chen, Z.; Spiryagin, M.; Bernal Arango, E. J.; Wolfs, P.; Cole, C.; Zhai, W. 2021. Dynamic response feature of electromechanical coupled drive subsystem in a locomotive excited by wheel flat, Engineering Failure Analysis 122: 105248. https://doi.org/10.1016/j.engfailanal.2021.105248
Asad, B.; Vaimann, T.; Belahcen, A.; Kallaste, A.; Rassõlkin, A.; Iqbal, M. N. 2020. The cluster computation-based hybrid fem–analytical model of induction motor for fault diagnostics, Applied Sciences 10(21): 7572. https://doi.org/10.3390/app10217572
Barkov, A. V.; Barkova, N. A. 2013. Vibracionnaja diagnostika mashin i oborudovanija. Analiz vibracii. Sankt-Peterburg: Sevzapuchcentr. 152 s. (in Russian).
Basaran, M.; Fidan, M. 2021. Induction motor fault classification via entropy and column correlation features of 2D represented vibration data, Eksploatacja i Niezawodność – Maintenance and Reliability 23(1): 132–142. https://doi.org/10.17531/ein.2021.1.14
Bodnar, B.; Ochkasov, O. 2021. Devising a procedure to form the diagnostic parameters for locomotives using a principal components analysis, Eastern-European Journal of Enterprise Technologies 2(1): 97–103. https://doi.org/10.15587/1729-4061.2021.230293
Bodnar, B.; Ochkasov, O.; Bobyr, D.; Korenyuk, R.; Bazaras, Ž. 2018a. Using the self-braking method when the post-overhaul diagnostics of diesel-hydraulic locomotives, in Transport Means 2018: Proceedings of the 22nd International Scientific Conference, 3–5 October 2018, Trakai, Lithuania, 2: 914–919.
Bodnar, B.; Ochkasov, O.; Chernyaev, D.; Skvireckas, R. 2018b. Use of the wavelet transform for the analysis of irregularity of crankshaft angular velocity, in Transport Means 2018: Proceedings of the 22nd International Scientific Conference, 3–5 October 2018, Trakai, Lithuania, 2: 962–967.
Bodnar, B. Je.; Ochkasov, O. B.; Chernjajev, D. V.; Shevchenko, Ja. I. 2013. Diagnostuvannja tjagovyh elektrodvyguniv za nerivnomirnistju obertannja jakorja, Nauka ta progres transportu 3(45): 13–21. https://doi.org/10.15802/stp2013/14793 (in Ukrainian).
Bodnar, B.; Ochkasov, O.; Ochkasov, M. 2021. Devising a procedure for calculating the technical condition index of locomotive nodes based on monitoring results, Eastern-European Journal of Enterprise Technologies 5(3): 37–45. https://doi.org/10.15587/1729-4061.2021.242478
Dacun, Ju. N. 2015. Vybor strategii tehnicheskogo obsluzhivanija i remonta lokomotivov na osnove metodov nechetkoj logiki, Visnyk Shidnoukrai’ns’kogo nacional’nogo universytetu imeni Volodymyra Dalja 1: 77–80. Available from Internet: http://nbuv.gov.ua/UJRN/VSUNU_2015_1_17 (in Russian).
Evgrafov, A. N.; Karazin, V. I.; Petrov, G. N. 2019. Analysis of the Self-braking Effect of Linkage Mechanisms, in A. N. Evgrafov (Ed.). Advances in Mechanical Engineering: Selected Contributions from the Conference “Modern Engineering: Science and Education”, Saint Petersburg, Russia, May 2018, 119–127. https://doi.org/10.1007/978-3-030-11981-2_11
Garramiola, F.; Poza, J.; Madina, P.; Del Olmo, J.; Almandoz, G. 2018. A review in fault diagnosis and health assessment for railway traction drives, Applied Sciences 8(12): 2475. https://doi.org/10.3390/app8122475
ISO 17359:2018. Condition Monitoring and Diagnostics of Machines. General Guidelines.
Kapitsa, M.; Laguta, V.; Kozik, Y. 2019. Classification of quality conditions of a traction motor frame insulation of locomotives, MATEC Web of Conferences 294: 03002. https://doi.org/10.1051/matecconf/201929403002
Kapitsa, M.; Laguta, V.; Kozik, Y. 2018. Selecting the parameters of the diagnosis of frame insulation condition in electrical machines of locomotives, International Journal of Engineering & Technology 7(4.3): 110–114. https://doi.org/10.14419/ijet.v7i4.3.19718
Kljushnyk, I. A. 2017. Doslidzhennja mozhlyvosti vykorystannja nejronnyh merezh pry vyprobuvannjah gidravlichnyh peredach teplovoziv, Informacijno-kerujuchi systemy na zaliznychnomu transporti (5): 8–15. https://doi.org/10.18664/ikszt.v0i5.115370 (in Ukrainian).
Li, Y.; Lu, A.; Wu, X.; Yuan, S. 2019. Dynamic anomaly detection using vector autoregressive model, Lecture Notes in Computer Science 11439: 600–611. https://doi.org/10.1007/978-3-030-16148-4_46
Lis, A.; Dworakowski, Z.; Czubak, P. 2021. An anomaly detection method for rotating machinery monitoring based on the most representative data, Journal of Vibroengineering 23(4): 861–876. https://doi.org/10.21595/jve.2021.21622
Lu, J.; Qian, W.; Li, S.; Cui, R. 2021. Enhanced k-nearest neighbor for intelligent fault diagnosis of rotating machinery, Applied Sciences 11(3): 919. https://doi.org/10.3390/app11030919
Luk’janov, A. V.; Perelygina, A. Ju.; Chegaev, N. S. 2017. Obrabotka dannyh vibracionnogo kontrolja vspomogatel’nyh mashin jelektrovozov, Sovremennye tehnologii. Sistemnyj analiz. Modelirovanie 3(55): 119–125. (in Russian).
Malla, C.; Panigrahi, I. 2019. Review of condition monitoring of rolling element bearing using vibration analysis and other techniques, Journal of Vibration Engineering & Technologies 7: 407–414. https://doi.org/10.1007/s42417-019-00119-y
Munir, M.; Siddiqui, S. A.; Chattha, M. A.; Dengel, A.; Ahmed, S. 2019. FuseAD: unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models, Sensors 19(11): 2451. https://doi.org/10.3390/s19112451
Pavlenko, T.; Shavkun, V.; Petrenko, A. 2017. Ways to improve operation reliability of traction electric motors of the rolling stock of electric transport, Eastern-European Journal of Enterprise Technologies 5(8): 22–30. https://doi.org/10.15587/1729-4061.2017.112109
Sakaidani, Y.; Kondo, M. 2018. Bearing fault detection for railway traction motors through leakage current, in 2018 XIII International Conference on Electrical Machines (ICEM), 3–6 September 2018, Alexandroupoli, Greece, 1768–1774. https://doi.org/10.1109/ICELMACH.2018.8506796
Saleem, A.; Diwakar, G.; Satyanarayana, M. R. S. 2012. Detection of unbalance in rotating machines using shaft deflection measurement during its operation, IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE) 3(3): 8–20. https://doi.org/10.9790/1684-0330820
Serdjuk, T. M. 2018. Diagnostuvannja tryfaznyh asynhronnyh dvyguniv, Elektromagnitna sumisnist’ ta bezpeka na zaliznychnomu transporti 16: 32–42. Available from Internet: http://ecsrt.diit.edu.ua/article/view/172537
Udovenko, S. G.; Kelembet, D. V.; Teslenko, O. V. 2020. Obrobka informacii’v skladnyh tehnichnyh systemah, Systemy obrobky informacii’ 1(160): 7–17. https://doi.org/10.30748/soi.2020.160.01 (in Ukrainian).
Venkatesan, S.; Manickavasagam, K.; Tengenkai, N.; Vijayalakshmi, N. 2019. Health monitoring and prognosis of electric vehicle motor using intelligent-digital twin, IET Electric Power Applications 13(9): 1328–1335. https://doi.org/10.1049/iet-epa.2018.5732
Zhao, Y.; Liu, P.; Wang, Z.; Zhang, L.; Hong, J. 2017. Fault and defect diagnosis of battery for electric vehicles based on big data analysis methods, Applied Energy 207: 354–362. https://doi.org/10.1016/j.apenergy.2017.05.139
Zhou, Z.; Chen, Z.; Spiryagin, M.; Bernal Arango, E. J.; Wolfs, P.; Cole, C.; Zhai, W. 2021. Dynamic response feature of electromechanical coupled drive subsystem in a locomotive excited by wheel flat, Engineering Failure Analysis 122: 105248. https://doi.org/10.1016/j.engfailanal.2021.105248