RESEARCH PAPER
Estimate and Extend the Time between Overhauls for the Railcar Body
1
Faculty of Mechanics and Technology, Rzeszow University of Technology, Poland
Submission date: 2024-08-11
Final revision date: 2024-09-15
Acceptance date: 2024-10-22
Online publication date: 2024-10-26
Publication date: 2024-10-26
Corresponding author
Eksploatacja i Niezawodność – Maintenance and Reliability 2025;27(2):195021
HIGHLIGHTS
- The work aimed to estimate and extend the time between overhauls of railcar body.
- A novel approach was proposed, incorporating an operational factor of safety.
- A new equation was introduced to determine the time between overhauls of railcar body.
- The proposed design extends time between overhauls by 1.21 to 2.06 times.
KEYWORDS
TOPICS
ABSTRACT
This paper addresses the challenge of estimating and extending the time between overhauls of railcar bodies, which are crucial components in rail freight transportation. Railcar bodies endure significant operational loads, leading to damage and the need for frequent repairs. In response to this issue, a novel approach has been proposed: the use of an operational factor of safety that considers both the probability of failure-free operation and the likelihood of load occurrence on the railcar body. A new equation has been introduced to determine the time between overhauls of these bodies. The focus of this paper is on the development of innovative engineering solutions aimed at enhancing the strength and reliability of railcar bodies. Theoretical and experimental studies were conducted to validate the effectiveness of the proposed design. The results demonstrate that the implementation of these solutions can extend the time between overhauls of the railcar body by 1.21 to 2.06 times, depending on operating conditions.
ACKNOWLEDGEMENTS
I would like to express my gratitude to Professor Denys Baranovskyi for his support and guidance in implementing the research.
REFERENCES (61)
1.
Baranovskyi D, Myamlin S, Podosonov D, Muradian L. Determination of the filler concentration of the composite material to reduce the wear of the central bowl of the rail truck bolster. Ain Shams Engineering Journal 2023; 14(12): 102232. https://doi.org/10.1016/j.asej....
2.
Panchenko S, Gerlici J, Lovska A, Ravlyuk V, Dižo J, Blatnický M. Analysis of asymmetric wear of brake pads on freight wagons despite full contact between pad surface and wheel. Symmetry 2024; 16 (3): 346. https://doi.org/10.3390/sym160....
3.
Chmielowiec A, Woś W, Gumieniak J. Viscosity Approximation of PDMS Using Weibull Function. Materials 2021; 14(20): 6060. https://doi.org/10.3390/ma1420....
4.
Gerlici J, Lovska A, Vatulia G, Pavliuchenkov M, Kravchenko O, Solčanský S. Situational Adaptation of the Open Wagon Body to Container Transportation. Applied Sciences 2023; 13(15): 8605. https://doi.org/10.3390/app131....
5.
Baranovskyi D, Myamlin S, Kebal I. Increasing the carrying capacity of the solid-body rail freight car. Advances in Science and Technology. Research Journal 2022; 16 (3): 219-225. https://doi.org/10.12913/22998....
6.
Park B N, Kim K H, Kim J A. Study on Determining the Optimal Replacement Interval of the Rolling Stock Signal System Component based on the Field Data. Journal of the Korean Society of Safety 2023; 38(2): 104-111. https://doi.org/10.14346/JKOSO....
7.
Sedghi M, Kauppila O, Bergquist B, Vanhatalo E, Kulahci M. A taxonomy of railway track maintenance planning and scheduling: A review and research trends. Reliability Engineering & System Safety 2021; 215: 107827. https://doi.org/10.1016/j.ress....
8.
Bressi S, Santos J, Losa M. Optimization of maintenance strategies for railway track-bed considering probabilistic degradation models and different reliability levels. Reliability Engineering & System Safety 2021; 207: 107359, https://doi.org/10.1016/j.ress....
9.
Allahvirdizadeh R, Andersson A, Karoumi R. Improved dynamic design method of ballasted high-speed railway bridges using surrogate-assisted reliability-based design optimization of dependent variables. Reliability Engineering & System Safety 2023; 238: 109406. https://doi.org/10.1016/j.ress....
10.
Baranovskyi D, Myamlin S, Bulakh M, Podosonov D, Muradian L. Determination of the Filler Concentration of the Composite Tape. Applied Science 2022; 12: 11044. https://doi.org/10.3390/app122....
11.
Saleh A, Remenyte-Prescott R, Prescott D, Chiachío M. Intelligent and adaptive asset management model for railway sections using the iPN method. Reliability Engineering & System Safety 2024; 241: 109687. https://doi.org/10.1016/j.ress....
12.
Konowrocki R, Chojnacki A. Analysis of rail vehicles’ operational reliability in the aspect of safety against derailment based on various methods of determining the assessment criterion. Eksploatacja i Niezawodność – Maintenance and Reliability 2020; 22 (1): 73–85. http://dx.doi.org/10.17531/ein....
13.
del Castillo A C, Marcos J A, Parlikad A K. Dynamic fleet maintenance management model applied to rolling stock. Reliability Engineering & System Safety 2023; 240: 109607. https://doi.org/10.1016/j.ress....
14.
Babishin V, Taghipour S. An algorithm for estimating the effect of maintenance on aggregated covariates with application to railway switch point machines. Eksploatacja i Niezawodność – Maintenance and Reliability 2019; 21(4): 619-630. https://doi.org/10.17531/ein.2....
15.
Luqman Hakim S, Kusumah L. Improving the Effectiveness of Rolling Stock Maintenance: A Systematic Literature Review. Proceedings of the 3rd Asia Pacific International Conference on Industrial Engineering and Operations Management, Johor Bahru, Malaysia, 2022. https://doi.org/10.46254/AP03.....
16.
Baranovskyi D, Myamlin S. The criterion of development of processes of the self organization of subsystems of the second level in tribosystems of diesel engine. Scientific Reports 2023; 13: 5736. https://doi.org/10.1038/s41598....
17.
Zhu X, Han C, Liu R, Yan G, Gu J. One universal method of complex system reliability, maintainability, supportability, testability quotas design and trade-off based on improved flower pollination algorithm. Quality and Reliability Engineering International 2021; 37(4): 1524-1543. https://doi.org/10.1002/qre.28....
18.
Krishnendu K, Sabitha P A, Drisya M, Joby K J. Inference on the Еime-dependent stress-strength reliability models based on finite mixture models. Reliability: Theory & Applications 2024; 1(77): 268-284, DOI: https://doi.org/10.24412/1932-....
19.
Jiang Q, Chen C-H. A numerical algorithm of fuzzy reliability. Reliability Engineering & System Safety 2003; 80(3): 299-307. https://doi.org/10.1016/S0951-....
20.
Veevers A, Obaid H A Z M. Reliability Modeling and Asset Management for Distribution Systems. International Journal of Reliability, Quality and Safety Engineering 1999; 06(03): 217-228. https://doi.org/10.1142/S02185....
21.
Bouillaut L, Francois O, Dubois S. A Bayesian network to evaluate underground rails maintenance strategies in an automation context. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 2013; 227(4): 411-424. https://doi.org/10.1177/174800....
22.
Kang R, Wang J, Cheng J, Chen J, Pang Y. Intelligent Forecasting of Automatic Train Protection System Failure Rate in China High-speed Railway. Eksploatacja i Niezawodność – Maintenance and Reliability 2019; 21(4): 567-576. https://doi.org/10.17531/ein.2....
23.
Nazarizadeh F, Alemtabriz A, Zandieh M, Raad A. An analytical model for reliability assessment of the rail system considering dependent failures (case study of Iranian railway). Reliability Engineering & System Safety 2022; 227: 108725. https://doi.org/10.1016/j.ress....
24.
Cremona M A, Liu B, Hu Y, Bruni S, Lewis R. Predicting railway wheel wear under uncertainty of wear coefficient, using universal kriging. Reliability Engineering & System Safety 2016;154: 49-59. https://doi.org/10.1016/j.ress....
25.
Zhang P, Zhang L, Wei D, Wu P, Cao J, Shijia C, Qu X. A high-performance copper-based brake pad for high-speed railway trains and its surface substance evolution and wear mechanism at high temperature. Wear 2020; 444-445: 203182, https://doi.org/10.1016/j.wear....
26.
Vo K D, Zhu H T, Tieu A K, Kosasih P B. Comparisons of stress, heat and wear generated by AC versus DC locomotives under diverse operational conditions. Wear 2015; 328-329: 186-196. https://doi.org/10.1016/j.wear....
27.
Breznická A, Mikuš P. The use of experimental modelling in the prediction of product reliability. Reliability: Theory & Applications 2024; 1(77): 310-319. DOI: https://doi.org/10.24412/1932-....
28.
Hu Z, Hu L, Wu S, Yu X.Reliability assessment of discrete-time k/n(G) retrial system based on different failure types and the δ-shock model. Reliability Engineering & System Safety 2024; 251: 110371, https://doi.org/10.1016/j.ress....
29.
Song C, Xiao R, Zhang C, Zhao X, Sun B. Simulation-free reliability analysis with importance sampling-based adaptive training physics-informed neural networks: Method and application to chloride penetration. Reliability Engineering & System Safety 2024; 246: 110083. https://doi.org/10.1016/j.ress....
31.
Jost D. Freight wagon with unloading device, especially for transport of bulk materials. European Patent Office; EP1520765B1: 2010-04-21.
32.
Jost D. Freight car in particular for carrying bulk materials. Poland; PL192909B1: 2006-12-29.
34.
Chmielowiec A. Algorithm for error-free determination of the variance of all contiguous subsequences and fixed-length contiguous subsequences for a sequence of industrial measurement data. Comput Stat 2021; 36: 2813-2840. https://doi.org/10.1007/s00180....
35.
Ye Y, Sun Y, Dongfang S, Shi D, Hecht M. Optimizing wheel profiles and suspensions for railway vehicles operating on specific lines to reduce wheel wear: a case study. Multibody System Dynamics 2021; 51: 91-122. https://doi.org/10.1007/s11044....
36.
Petrenko V O, Ishchenko V M. Strengthening riveted joints of the grain rail car backstop. Strength of Materials 2023; 55: 1192-1200. https://doi.org/10.1007/s11223....
37.
Fomin O, Gorbunov M, Gerlici J, Vatulia G, Lovska A, Kravchenko K. Research into the strength of an open wagon with double sidewalls filled with aluminium foam. Materials 2021; 14: 3420. https://doi.org/10.3390/ma1412....
38.
Łagoda T, Małecka J, Małys S, Derda S, Kuś J, Krysiński P. Fatigue fracture cross-sections after cyclic tests with a combination of cyclic bending and torsion of samples made of aluminum alloy 6060. Eksploatacja i Niezawodność – Maintenance and Reliability 2024; 26(1). https://doi.org/10.17531/ein/1....
39.
Baranovskyi D, Bulakh M, Myamlin S, Kebal I. New Design of the Hatch Cover to Increase the Carrying Capacity of the Gondola Car. Adv. Sci. Technol. Res. J. 2022; 16: 186-191. https://doi.org/10.12913/22998....
40.
Irikovich Z, Vyacheslavovich R, Mahmod W. Development of new polymer composite materials for the flooring of rail carriage. International Journal of Engineering & Technology 2020; 9(2): 378-381. https://doi.org/10.14419/ijet.....
41.
Saeedi A, Motavalli M, Shahverdi M. Recent advancements in the applications of fiber-reinforced polymer structures in railway industry – A review. Polymer Composites 2024; 45(1): 77-97. https://doi.org/10.1002/pc.278....
42.
Florczak Ł, Kościelniak B, Kramek A, Sobkowiak A. The influence of potassium hexafluorophosphate on the morphology and anticorrosive properties of conversion coatings formed on the AM50 magnesium alloy by plasma electrolytic oxidation. Materials 2023; 16(24): 7573. https://doi.org/10.3390/ma1624....
43.
Eleftheroglou N, Galanopoulos G, Loutas T. Similarity learning hidden semi-Markov model for adaptive prognostics of composite structures. Reliability Engineering & System Safety 2024; 243: 109808, https://doi.org/10.1016/j.ress....
44.
Bulakh M, Klich L, Baranovska O, Baida A, Myamlin S. Reducing Traction Energy Consumption with a Decrease in the Weight of an All-Metal Gondola Car. Energies 2023; 16: 6733. https://doi.org/10.3390/en1618....
45.
Plota A, Masek A. Lifetime Prediction Methods for Degradable Polymeric Materials – A Short Review. Materials 2020; 13: 4507. https://doi.org/10.3390/ma1320....
46.
Barbosa J F, Correia J A F O, Freire R C S, De Jesus A M P. Fatigue life prediction of metallic materials considering mean stress effects by means of an artificial neural network. International Journal of Fatigue 2020; 135: 105527. https://doi.org/10.1016/j.ijfa....
47.
Kalayci C B, Karagoz S, Karakas Ö. Soft computing methods for fatigue life estimation: A review of the current state and future trends. Fatigue & Fracture of Engineering Materials & Structures 2020; 43(12): 2763-2785. https://doi.org/10.1111/ffe.13....
48.
Otte T, Posada-Moreno A F, Hübenthal F, Haßler M, Bartels H, Abdelrazeq A, Hees F. Condition monitoring of rail infrastructure and rolling stock using acceleration sensor data of on-rail freight wagons. ICPRAM 2022 - 11th International Conference on Pattern Recognition Applications and Methods: 432-439. https://doi.org/10.5220/001082....
49.
Bernal E, Spiryagin M, Cole C. Ultra-Low power sensor node for on-board railway wagon monitoring. IEEE Sensors Journal 2020; 20(24): https://doi.org/10.1109/JSEN.2....
50.
Kostrzewski M, Melnik R. Condition monitoring of rail transport systems: a bibliometric performance analysis and systematic literature review. Sensors 2021; 21(14): 4710. https://doi.org/10.3390/s21144....
51.
Brezulianu A, Aghion C, Hagan M, Geman O, Chiuchisan I, Balan A-L, Balan D-G, Balas VE. Active control parameters monitoring for freight trains, using wireless sensor network platform and internet of things. Processes 2020; 8(6): 639. https://doi.org/10.3390/pr8060....
52.
He W, Shi W, Le J, Li H, Ma R. Geophone-based energy harvesting approach for railway wagon monitoring sensor with high reliability and simple structure. IEEE Access 2020; 8: 35882-35891. https://doi.org/10.1109/ACCESS....
53.
Wang Q, Li D, Zeng J, Peng X, Wei L, Du W. A diagnostic method of freight wagons hunting performance based on wayside hunting detection system. Measurement 2024; 227: 114274. https://doi.org/10.1016/j.meas....
54.
Gao M, Cong J, Xiao J, He Q, Li S, Wang Y, Yao Y, Chen R, Wang P. Dynamic modeling and experimental investigation of self-powered sensor nodes for freight rail transport. Applied Energy 2020; 257: 113969. https://doi.org/10.1016/j.apen....
55.
Zanelli F, Sabbioni E, Carnevale M, Mauri M, Tarsitano D, Castelli-Dezza F, Debattisti N. Wireless sensor nodes for freight trains condition monitoring based on geo-localized vibration measurements. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 2023; 237(2): 193-204. https://doi.org/10.1177/095440....
56.
Zhang Z, Kong L, Zhu Z, Wu X, Li H, Zhang Z, Yan J. An electromagnetic vibration energy harvesting system based on series coupling input mechanism for freight railroads. Applied Energy 2024; 353 Part A: 122047. https://doi.org/10.1016/j.apen....
57.
Panchenko S, Fomin O, Vatulia G, Ustenko O, Lovska A. Determining the load on the long-based structure of the platform car with elastic elements in longitudinal beams. Eastern-European Journal of Enterprise Technologies 2021; 1(7): 109. https://doi.org/10.15587/1729-....
58.
Nandan S, Trivedi R, Kant S, Ahmad J, Maniraj M. Design, analysis and prototype development of railway wagons on different loading conditions. International Journal of Engineering Applied Sciences and Technology 2020; 4(10): 122-129.
59.
Shvets A O. Dynamic indicators influencing design solution for modernization of the freight rolling stock. FME Transactions 2021; 49(3): 673-683. https://doi.org/10.5937/fme210....
60.
Sulym A, Safronov O, Strynzha A, Khozia P. Ways of improving of freight car design. Transport Systems and Technologies 2024; 43: 47-60. https://doi.org/10.32703/2617-....
61.
Lipcinski D, Ovezea D, Ilie C I, Tudor E, Albei V-E, Popa M, Drețcanu S, Tănase N, Mihai R M, Nicolaie S. Development and implementation of an energy-efficient test stand for railway car bogies. Electrotehnica, Electronica, Automatica 2023; 71(4): 03-14. https://doi.org/10.46904/eea.2....
CITATIONS (1):
1.
Experimental research of the stress-strain state of the energyabsorbing center sill of an open wagon
G L Vatulia, A O Lovska, A V Rybin, M V Pavliuchenkov, D H Petrenko
IOP Conference Series: Earth and Environmental Science
G L Vatulia, A O Lovska, A V Rybin, M V Pavliuchenkov, D H Petrenko
IOP Conference Series: Earth and Environmental Science
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