Abstract
The failures of rolling bearings of asynchronous traction motors of locomotives are studied. The malfunctions of rolling bearing units of locomotives are analyzed. The vibration and current signals and the corresponding frequency spectra of an electric motor when operating in normal conditions, as well as with bearing failures, are presented. A model to assess the technical condition of locomotive rolling bearings is developed, and the feasibility of proactive diagnosis, which makes it possible to identify defects in advance at an early stage of their development, is substantiated. The results of the study can be used in systems of real-time diagnosis of the technical condition of rolling bearings of asynchronous traction motors of locomotives.
Similar content being viewed by others
REFERENCES
Grishchenko, A.V. and Kozachenko, E.V., Novye elektricheskie mahsiny lokomotivov (New Electric Machines for Locomotives), Moscow: Uch.-Metod. Tsentr Obraz. Zheleznodorozhn. Transp., 2008.
Grishchenko, A.V., Grachev, V.V., Babkov, Yu.V., Klimenko, Yu.I., Kim, S.I., Perfil’ev, K.S., and Fedotov, M.V., Artificial neural network apparatus for diagnostics of a modern locomotive, Lokomotiv, 2012, no. 7.
Khamidov, O.R. and Kasymov, O.T., Razrabotka metodiki kompleksnogo diagnostirovaniya asinkhronnogo tyagovogo elektrodvigatelya podvizhnogo sostava zheleznodorozhnogo transporta (A Method for Complex Diagnostics of an Asynchronous Traction Electric Motor of a Rolling Stock of Railway Transport), St. Petersburg: Natsrazvitie, 2017.
Grachev, V.V., Grishchenko, A.V., and Bazilevskii, F.Yu., Reliability of direct methods of operational control of the energy efficiency of operated diesel locomotives, Vestn. Inst. Probl. Estestv. Monopolii: Tekh. Zhelezn. Dorog, 2018, no. 2 (42).
Shraiber, M.A., Grishchenko, A.V., Grachev, V.V., and Kruchek, V.A., Improvement of the efficiency of maintenance service of locomotives, Izv. Peterb. Gos. Univ. Putei Soobshch., 2012, no. 4.
Prieto, D., Cirrincione, G., Espinosa, G., Ortega, A., and Henao, H., Bearing fault detection by a novel condition-monitoring scheme based on statistical-time features and neural networks, IEEE Trans. Ind. Electron., 2013, vol. 60, no. 8.
Ghate, V.N. and Dudul, V., Induction machine fault detection using generalized feed forward neural network, J. Electr. Eng. Technol., 2009, vol. 4, no. 3.
Briz, F., Degner, M., Garcia, P., and Bragado, D., Broken rotor bar detection in line-fed induction machines using complex wavelet analysis of startup transients, IEEE Trans. Ind. Appl., 2008, vol. 44.
Ghate, V.N. and Dudul, S.V., Optimal MLP neural network classifier for fault detection of three phase induction motor, Expert Syst. Appl., 2010, vol. 37.
Author information
Authors and Affiliations
Corresponding author
Additional information
Translated by N. Semenova
About this article
Cite this article
Grishchenko, A.V., Kruchek, V.A., Kurilkin, D.N. et al. Diagnostics of the Technical Condition of Rolling Bearings of Asynchronous Traction Motors of Locomotives Based on Data Mining. Russ. Electr. Engin. 91, 593–596 (2020). https://doi.org/10.3103/S1068371220100041
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.3103/S1068371220100041