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Abstrakty
The faulty running gear of rail vehicles can be identified by the results of measurements of vertical forces caused by wheel running surface damages or other wrongness of bogie suspension. Several different automatic diagnostic systems are used in European railways (operating while the trains are in service) to detect damage to rail-wheel running surfaces. The principles of this trackside equipment operation and the reliability of their measurements may differ noticeably. This is especially true in different seasons of the year (winter/summer). Data collection and aggregation results should be checked in equivalency (comparability). The authors compared the efficiency of different automatic systems in detecting wheel failures according to wheel-rail loads in different seasons of the year and presented their results. The authors also compared the similarity of results of the different measurement systems.
Czasopismo
Rocznik
Tom
Strony
67--78
Opis fizyczny
Bibliogr. 24 poz.
Twórcy
autor
- Vilnius Gediminas Technical University (VILNIUS TECH); Saulėtekio al. 11, 10223 Vilnius, Lithuania
autor
- Vilnius Gediminas Technical University (VILNIUS TECH); Saulėtekio al. 11, 10223 Vilnius, Lithuania
autor
- Vilnius Gediminas Technical University (VILNIUS TECH); Saulėtekio al. 11, 10223 Vilnius, Lithuania
autor
- EURNEX e.V., European Rail Research Network of Excellence; Hardenbergstrasse 12, 10623 Berlin, Germany
Bibliografia
- 1. Ramalho, A. Wear modelling in rail-wheel contact. Wear. 2015. Vol. 330-331. P. 524-532.
- 2. Thakkar, N.A. & Steel, J.A. & Reuben, R.L. Rail-wheel interaction monitoring using Acoustic Emission: A laboratory study of normal rolling signals with natural rail defects. Mech Syst Signa Process. 2010. Vol. 24. No. 1. P. 256-266.
- 3. Roy, T. & Lai, Q. & Abrahams, R. & Mutton, P. & Paradowska, A. & Soodi, M. & et al. Effect of deposition material and heat treatment on wear and rolling contact fatigue of laser cladded rails. Wear. 2018. Vol. 412-413. P. 69-81.
- 4. Wang, W.J. & Liu, T.F. & Wang, H.Y. & Liu, Q.Y. & Zhu, M.H. & Jin, X.S. Influence of friction modifiers on improving adhesion and surface damage of wheel/rail under low adhesion conditions. Tribol Int. 2014. Vol. 75. P. 16-23.
- 5. Vaičiūnas, G. & Bureika, G. & Steišūnas, S. Research on metal fatigue of rail vehicle wheel considering the wear intensity of rolling surface. Eksploatacja i Niezawodnosc. 2018. Vol. 20. No. 1. P. 24-29.
- 6. Huang, Y.B. & Shi, L.B. & Zhao, X.J. & Cai, Z.B. & Liu, Q.Y. & Wang, W.J. On the formation and damage mechanism of rolling contact fatigue surface cracks of wheel/rail under the dry condition. Wear. 2018. Vol. 400-401. P. 62-73.
- 7. Dižo, J. & Blatnický, M. & Gerlici, J. & Leitner, B. & Melnik, R. & Semenov, S. & et al. Evaluation of Ride Comfort in a Railway Passenger Car Depending on a Change of Suspension Parameters. Sensors. 2021. Vol. 21. No. 23. Paper No. 8138.
- 8. Bureika, G. & Levinzon, M. & Dailydka, S. & Steisunas, S. & Zygiene, R. Evaluation criteria of wheel/rail interaction measurement results by trackside control equipment. Int J Heavy Veh Syst. 2019. Vol. 26. No. 6. P. 747-764.
- 9. Zhang, S. & Cheng, G. & Sheng, X. & Thompson, D.J. Dynamic wheel-rail interaction at high speed based on time-domain moving Green’s functions. J Sound Vib. 2020. Vol. 488. No. 115632.
- 10. Gu, Q. & Liu, Y. & Guo, W. & Li, W. & Yu, Z. & Jiang, L. A Practical Wheel-Rail Interaction Element for Modeling Vehicle-Track-Bridge Systems. International Journal of Structural Stability and Dynamics. 2019. Vol. 19. No. 2. No. 1950011.
- 11. Infante, V. & Freitas, M. & Baptista, R. Failure analysis of a parabolic spring belonging to a railway wagon. Eng Fail Anal. 2022. Vol. 140. No. 106526.
- 12. Prithvi, C. & Srinidhi, R. & Karthik Hebbar, A. Vibration Analysis of Railway Wagon Suspension System for Improved Ride Quality using MATLAB Simulink. Operations Management and Systems Engineering. Lecture Notes on Multidisciplinary Industrial Engineering book series (LNMUINEN). Springer. P. 95-112.
- 13. Kandekar, P. & Patil, B. & Kulkarni, K. & Kotyal, R. & Joshi, S. & Patil, A.Y. & et al. Design and development of alternative method for vibration issues in Locomotive Wagon wheels. J Phys Conf Ser. 2020. Vol. 1706. No. 1. No. 012178.
- 14. Mazilu, T. & Dinu, V.M. The vibration behaviour of a freight wagon in the presence of irregularities of the track. IOP Conf Ser Mater Sci Eng. 2019. Vol. 682. No. 1. No. 012007.
- 15. Dizo, J. & Blatnický, M. & Pavlík, A. Process of modelling the freight wagon multibody system and analysing its dynamic properties by means of simulation computations. MATEC Web Conf. 2018. Vol. 235. No. 00027.
- 16. Chang, C. & Ling, L. & Chen, S. & Zhai, W. & Wang, K. & Wang, G. Dynamic performance evaluation of an inspection wagon for urban railway tracks. Measurement. 2021. Vol. 170. No. 108704.
- 17. Pandya, D.H. & Dipak Gadhia, U. & Darji, V.P. & Pandya, D.H. Dynamic Analysis of Quarter Model Simulation for Wagon-R Car in ADAMS. International Journal of Research and Analytical Reviews. 2019. P. 1-5.
- 18. Jonsson, P.A. & Andersson, E. & Stichel, S. Influence of link suspension characteristics variation on two-axle freight wagon dynamics. International Journal of Vehicle Mechanics and Mobility. 2006. Vol. 44. No. 1. P. 415-423.
- 19. Gorbunov, M. & Gerlici, J. & Kara, S. & Nozhenko, O. & Chernyak, G. & Kravchenko, K. & Lack, T. New principle schemes of freight cars bogies. Manufacturing Technology. 2018. Vol. 18. No. 2. P. 233-238.
- 20. Pandey, M. & Bhattacharya, B. Effect of bolster suspension parameters of three-piece freight bogie on the lateral frame force. Int J Rail Transp. 2020. Vol. 8. No. 1. P. 45-65.
- 21. Mosleh, A. & Meixedo, A. & Ribeiro, D. & Montenegro, P. & Calçada, R. Automatic clusteringbased approach for train wheels condition monitoring. International Journal of Rail Transportation. 2022. P. 1-26. DOI: 10.1080/23248378.2022.2096132.
- 22. Mosleh, A. & Meixedo, A. & Ribeiro, D. & Montenegro, P. & Calçada, R. Early wheel flat detection: an automatic data-driven wavelet-based approach for railways. International Journal of Vehicle Mechanics and Mobility. 2022. DOI: 10.1080/00423114.2022.2103436.
- 23. Wang, Y.W. & Ni, Y.Q. & Wang, X. Real-time defect detection of high-speed train wheels by using Bayesian forecasting and dynamic model. Mech Syst Signal Process. 2020. Vol. 139. No. 106654.
- 24. Wang, G. & Cai, G. & Yin, X. A study on detection technology of rail transit vehicle wheel web based on lamb wave. Lect Notes Electr Eng. 2020. Vol. 640. P. 735-743.
Uwagi
PL
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-ae828fe1-136d-48a6-96cb-03871ac1ac38