Keywords

Rail transit, Intelligent operation and maintenance, Electric locomotive

References

[1] Lawrence, M., Bullock, R., & Liu, Z. (2019). China’s high-speed rail development. World Bank Publications.

[2] Altıntaş, O., Aksoy, M., Ünal, E., Akgöl, O., & Karaaslan, M. (2019). Artificial neural network approach for locomotive maintenance by monitoring dielectric properties of engine lubricant. Measurement, 145, 678-686. 

[3] Argyropoulou, K., Iliopoulou, C., & Kepaptsoglou, K. (2019). Model for corrective maintenance scheduling of rail transit networks: Application to athens metro. Journal of Infrastructure Systems, 25(1), 04018035. 

[4] Kou, L. (2022). A review of research on detection and evaluation of the rail surface defects. Acta Polytech. Hung, 19, 167-186. 

[5] Zheng, X., Yuan, J., & Qu, K. (2021). Research on Optimal Configuration of High Speed Railway Energy Storage System Considering Full Life Cycle. In The Proceedings of the 9th Frontier Academic Forum of Electrical Engineering: Volume I (pp. 569-579). Springer Singapore. 

[6] Pastukhov, K., Istomin, S., & Yrasov, O. (2023). Development of On-Board Systems of Predictive Diagnostics of Electric Rolling Stock Traction Motor. In A. Guda (Ed.), Networked Control Systems for Connected and Automated Vehicles (pp. 147-156). Springer International Publishing. 

[7] Kutovoj, Y., Kyrylenko, Y., Obruch, I., & Kunchenko, T. (2021). Application of Intelligent Control Systems in Electric Drives of Rail Vehicles. 2021 IEEE 2nd KhPI Week on Advanced Technology (KhPIWeek), 709-713.

[8] Tretyakov, E., Solovyov, D., & Kudinov, M. (2023). The Improvement of On-Board Systems for Predictive Technical Diagnostics of Mainline Electric Freight Locomotives Based on Digital Models. In A. Guda (Ed.), Networked Control Systems for Connected and Automated Vehicles (pp. 193-202). Springer International Publishing.