“Assessment of the residual service life of wheeled motor units: methods, problems and prospects”
Keywords:
wheel motor block, residual life, diagnostics, statistical methods, modeling, artificial intelligence, maintenance, vibration analysis, electromechanical system, technical reliabilityAbstract
This article analyzes existing scientific approaches to assessing the residual life of wheel motor blocks, including statistical, diagnostic, modeling, and artificial intelligence-based methods. The advantages of each method are examined alongside their practical limitations. The author provides recommendations for implementing hybrid approaches, multi-parameter assessment systems, and the development of practical data bases tailored to the operational conditions in Uzbekistan. The article aims to improve the efficiency of technical maintenance, enable early fault detection, and ensure the operational reliability of motor blocks.
References
1. Mikheev, V. I., et al. (2020). “Reliability assessment of electric motors used in traction systems.” Journal of Transport Engineering, 45(2), 132-145.
2. Santos, P. R., et al. (2019). “Modeling of electric machine degradation in traction applications.” IEEE Transactions on Industrial Electronics, 66(11), 902-911.
3. Pereira, R. L., et al. (2021). “Artificial Intelligence for predictive maintenance of electric motors in railway systems.” International Journal of Electrical Engineering, 12(4), 378-385.
4. Kuznetsov, V. A., & Belyaev, S. A. (2018). “Diagnostics and monitoring of electric traction motors.” Journal of Railway Engineering and Technology, 39(3), 99-110.
5. Li, Y., & Wang, D. (2022). “Advanced techniques for assessing electric motor life in railway systems.” IEEE Transactions on Power Electronics, 56(7), 2161-2171.
6. Jia, Z., & Liu, Q. (2023). “Predictive maintenance for electric drive systems in railways: A review.” Electric Power Systems Research, 187, 106-119.
