Assessment of durability and reliability of ET41 series locomotive wheels based on laboratory tests
 
More details
Hide details
1
Silesian University of Technology
CORRESPONDING AUTHOR
Mirosław Witaszek   

Silesian University of Technology
Submission date: 2020-07-20
Final revision date: 2020-11-16
Acceptance date: 2020-11-16
Online publication date: 2020-11-17
Publication date: 2020-11-17
 
Diagnostyka 2020;21(4):57–66
 
KEYWORDS
TOPICS
ABSTRACT
The article presents the method of determination of life and reliability of rail vehicles wheels on the basis of the wear model developed on the basis of laboratory tests results. The method allowed quantitative assessment of the impact of various factors on the life of the wheels due to flange and tread wear. It was stated that the most significant impact on life resulting from the flange wear is exerted by the share of curves in track length, whereas life due to tread wear – longitudinal creepage of the tread with regard to the rail. The reliability assessment was performed using the Monte Carlo method. It allows to take into account randomness of both interaction conditions and the wear process. The determined reliability was described by the Weibull’s distribution. The calculations were made for the ET41 series electrical freight locomotives. The method presented in this work can be suitable for preparation of schedules of wheel repairs and thus contribute to the increase of ride safety and comfort and therefore to the decrease of the costs of maintenance of the rail vehicles.
 
REFERENCES (30)
1.
Adamiec P, Witaszek K, Witaszek M. Service wear and durability of rail wheels. 12th International Colloquium Tribology. 2000 – Plus. Esslingen 11 - 13.01.2000: 1489-1494. German.
 
2.
Boyacioglu P, Bevan A. Prediction of rail damage using a combination of Shakedown Map and wheel-rail contact energy. Wear 2020; 460–461: 203457. https://doi.org/10.1016/j.wear....
 
3.
Christoforou P, Fletcher DI, Lewis R. Benchmarking of premium rail material wear. Wear. 2019; 436–437: 202990. https://doi.org/10.1016/j.wear....
 
4.
Ding HH, Fu ZK, Wang WJ, Guo J, Liu QY, Zhu MH. Investigation on the effect of rotational speed on rolling wear and damage behaviors of wheel/rail materials. Wear 2015; 330-331: 563–570. https://doi.org/10.1016/j.wear....
 
5.
Faccoli M, Petrogalli C, Lancini M, Ghidini A, Mazzù A. Effect of desert sand on wear and rolling contact fatigue behaviour of various railway wheel steels. Wear. 2018; 396–397: 46-161. https://doi.org/10.1016/j.wear....
 
6.
Granham JE, Beynon JH. Dry rolling -sliding wear of bainitic and pearlitic steels. Wear. 1992; 157: 81-109. https://doi.org/10.1016/0043-1....
 
7.
Hu Y, Guo LC, Maiorino M, Liu JP, Ding HH, Lewis R, Meli E, Rindi A, Liu QY, Wang WJ. Comparison of wear and rolling contact fatigue behaviours of bainitic and pearlitic rails under various rolling-sliding conditions, Wear. 2020; 460–461: 203455. https://doi.org/10.1016/j.wear....
 
8.
Hu Y, Zhou L, Ding HH, Lewis R, Liu QY, Guo J, Wang WJ. Microstructure evolution of railway pearlitic wheel steels under rolling-sliding contact loading. Tribology International. 2021; 154: 106685. https://doi.org/10.1016/j.trib....
 
9.
Jia X. Reliability analysis for Weibull distribution with homogeneous heavily censored data based on Bayesian and least-squares methods. Applied Mathematical Modelling. 2020; 83: 169-188. https://doi.org/10.1016/j.apm.....
 
10.
Kamiński W. Comparison of selected railway lines in Poland using the Analytical Hierarchy Process method. Scientific Journal of Silesian University of Technology. Series Transport. 2020; 108: 73-84. https://doi.org/10.20858/sjsut....
 
11.
Lesiak P, Sokołowski A, Wlazło M. Cross-correlation function in identifying head checking defects of the railway rails. Diagnostyka. 2017; 18 (2): 65-7.
 
12.
Li G, Hong Z, Yan Q. The influence of microstructure on the rolling contact fatigue of steel for high-speed-train wheel. Wear. 2015; 342-343: 349–355. https://doi.org/10.1016/j.wear....
 
13.
Luo R, Shi H, Teng W, Song Ch. Prediction of wheel profile wear and vehicle dynamics evolution considering stochastic parameters for high-speed train. Wear. 2017; 392–393: 126–138. https://doi.org/10.1016/j.wear....
 
14.
Maya-Johnson S, Felipe Santa J, Toro A. Dry and lubricated wear of rail steel under rolling contact fatigue - Wear mechanisms and crack growth. Wear 2017; 380–381: 240-250. https://doi.org/10.1016/j.wear....
 
15.
Mazzù A, Provezza L, Zani N, Petrogalli C, Ghidini A, Faccoli M. Effect of shoe braking on wear and fatigue damage of various railway wheel steels for high speed applications. Wear. 2019; 434–435: 203005. https://doi.org/10.1016/j.wear....
 
16.
Mesaritis M, Shamsa M, Cuervo P, Santa JF, Toro A, Marshall MB, Lewis R. A laboratory demonstration of rail grinding and analysis of running roughness and wear. Wear. 2020; 456–457: 203379. https://doi.org/10.1016/j.wear....
 
17.
Ramalho A. Wear modelling in rail–wheel contact. Wear 2015; 330-331: 524–532. https://doi.org/10.1016/j.wear....
 
18.
Sęk J. Innovative technologies in low-emission transport. Scientific Journal of Silesian University of Technology. Series Transport. 2020; 107: 165-175. https://doi.org/10.20858/sjsut....
 
19.
Shebani A, Iwnicki S. Prediction of wheel and rail wear under different contact conditions using artificial neural networks, Wear. 2018; 406–407: 173-184. https://doi.org/10.1016/j.wear....
 
20.
Spangenberg U, Fröhling RD, Schalk Els PS. The effect of rolling contact fatigue mitigation measures on wheel wear and rail fatigue. Wear. 2018; 398–399: 56-68. https://doi.org/10.1016/j.wear....
 
21.
Stastniak P, Smetanka L, Drozdziel P. Computer aided simulation analysis for wear investigation of railway wheel running surface. Diagnostyka. 2019; 20 (3): 63-68. https://doi.org/10.29354/diag/....
 
22.
Tressia G, Sinatora A, Goldenstein H, Masoumi M. Improvement in the wear resistance of a hypereutectoid rail via heat treatment. Wear 2020; 442–443: 203122. https://doi.org/10.1016/j.wear....
 
23.
Urda P, Aceituno JF, Muñoz S, Escalona JL. Artificial neural networks applied to the measurement of lateral wheel-rail contact force: A comparison with a harmonic cancellation method. Mechanism and Machine Theory. 2020; 153: 103968. https://doi.org/10.1016/j.mech....
 
24.
Vira V, Kulyk V, Chepil R, Kharchenko Y, Duriagina Z. The diagnostics and ways heat treatment optimization of a railway wheels steel. Diagnostyka. 2019; 20 (2): 105-111. https://doi.org/10.29354/diag/....
 
25.
Wang WJ, Lewis R, Yang B, Guo LC, Liu QY, Zhu MH. Wear and damage transitions of wheel and rail materials under various contact conditions. Wear. 2016; 362–363: 146-152. https://doi.org/10.1016/j.wear....
 
26.
Wojciechowski Ł, Gapiński B, Firlik B, Mathia TG. Characteristics of tram wheel wear: Focus on mechanism identification and surface topography. Tribology International. 2020; 150: 106365. https://doi.org/10.1016/j.trib....
 
27.
Xia F, Cole C, Wolfs P. The dynamic wheel–rail contact stresses for wagon on various tracks. Wear. 2008; 265: 1549–1555.
 
28.
Yazici O, Yilmaz S. Investigation of effect of various processing temperatures on abrasive wear behaviour of high power diode laser treated R260 grade rail steels. Tribology International. 2018; 119: 222-229. https://doi.org/10.1016/j.trib....
 
29.
Zhang Q, Toda-Caraballo I, Dai G, Feng Z, Li Q, Yu D. Influence of laminar plasma quenching on rolling contact fatigue behaviour of high-speed railway wheel steel. International Journal of Fatigue. 2020; 137: 105668. https://doi.org/10.1016/j.ijfa....
 
30.
Zhou W, Abdulhakeem S, Fang C, Han T, Li G, Wua Y, Faisal Y. A new wayside method for measuring and evaluating wheel-rail contact forces and positions. Measurement. 2020; 166: 108244. https://doi.org/10.1016/j.meas....
 
eISSN:2449-5220
ISSN:1641-6414