This paper undertakes a linear mixed model analysis framework to explore the impact of maintenance decisions on the turning (re-profiling) of freight railway wheelsets. Univariate and multivariate linear models analyzed all the different variables (including fixed and random factors) that mainly impact maintenance and life cycle. The impact of maintenance is based on response variables such as changes in wheel diameter, in flange thickness, in flange height, and in the flange slope. All response variables are considered individually and combined to produce different statistical model specifications. The evaluation and comparison of the different model specifications are conducted with the Akaike Information Criterion (AIC). The results show that (i) the multivariate model is the most suitable and (ii) the overall AIC value of a multivariate model is lower than its sum for the univariate linear mixed models.
TutcherJEastonJMRobertsC.Enabling data integration in the rail industry using RDF and OWL: the RaCoOn ontology. ASCE-ASME J Risk Uncertain Eng Syst Part A Civ Eng2017; 3: 1–12.
2.
AndradeARTeixeiraPF.Statistical modelling of railway track geometry degradation using Hierarchical Bayesian models. Reliab Eng Syst Saf2015; 142: 169–183.
3.
AndradeARTeixeiraPF.Biobjective optimization model for maintenance and renewal decisions related to rail track geometry. Transp Res Rec2011; 2261: 163–170.
4.
BragaJAPAndradeAR. Optimizing maintenance decisions in railway wheelsets: a Markov decision process approach. Proc IMechE, Part O: J Risk and Reliability2019; 233: 285–300.
5.
BragaJAPAndradeAR. Multivariate statistical aggregation and dimensionality reduction techniques to improve monitoring and maintenance in railways: the wheelset component. Reliab Eng Syst Saf2021; 216: 107932.
6.
CaldeiraGACBragaJAPAndradeAR. Assessing wear evolutions in railway wheelsets using a survival modeling approach. ASCE-ASME J Risk Uncertain Eng Syst Part B Mech Eng2021; 7: 41001.
7.
European Commission. Roadmap to a Single European Transport Area-Towards a competitive and resource efficient transport system. European Commission, 2011.
8.
IslamDMZRicciSNelldalB-L. How to make modal shift from road to rail possible in the European transport market, as aspired to in the EU Transport White Paper 2011. Eur Transp Res Rev2016; 8: 1–14.
9.
European Court of Auditors. Special Report 08/2023: intermodal freight transport: EU still far from getting freight off the road. European Court of Auditors, 2023; 1–69.
10.
ZengJZhangXJinK.Research on differential pricing and train operation decisions for railway cargo transportation under competitive conditions. Transp Res Rec2023; 2677: 860–874.
11.
El YaagoubiAFerjaniAEssaghirY, et al. A logistic model for a french intermodal rail/road freight transportation system. Transp Res Part E Logist Transp Rev2022; 164: 102819.
12.
KwilinskiALyulyovOPimonenkoT.Environmental sustainability within attaining sustainable development goals: the role of digitalization and the transport sector. Sustainability2023; 15: 11282.
13.
KendraMSkrúcanýTDolinayováA, et al. Environmental burden of different transport modes–real case study in Slovakia. Transp Res part D Transp Environ2023; 114: 103552.
14.
European Environment Agency. Report No 02/2022. Decarbonising road transport - the role of vehicles, fuels and transport demand. Transport and environment report 2021. EEA, 2022; 1–92.
15.
RitchieHRoserMRosadoP.CO2 and greenhouse gas emissions. Our world in data2020.
16.
KouJZhangJZhouH, et al. Effect of the worn status of wheel/rail profiles on wheel wear over curved tracks. J Mech Sci Technol2021; 35: 945–954.
17.
JiangZBanjevicDMingchengEM, et al. Optimizing the re-profiling policy regarding metropolitan train wheels based on a semi-Markov decision process. Proc IMechE, Part O: J Risk and Reliability2017; 231: 495–507.
18.
EN BS. 13715: 2006+ A1: 2010–Railway applications–Wheelsets and bogies–Wheels–Tread profile. Tech Comm CEN/TC; 256.
19.
LinJAsplundaMParidaaA.Reliability analysis for degradation of locomotive wheels using parametric bayesian approach. Qual Reliab Eng Int2014; 30: 657–667.
20.
FreitasMADe ToledoMLGColosimoEA, et al. Using degradation data to assess reliability: a case study on train wheel degradation. Qual Reliab Eng Int2009; 25: 607–629.
21.
AsplundMPaloMFamurewaS, et al. A study of railway wheel profile parameters used as indicators of an increased risk of wheel defects. Proc IMechE, Part F: J Rail and Rapid Transit2016; 230: 323–334.
22.
de Almeida CostaMde Azevedo Peixoto BragaJPRamos AndradeA. A data-driven maintenance policy for railway wheelset based on survival analysis and Markov decision process. Qual Reliab Eng Int2021; 37: 176–198.
23.
ZhangYAnZLuM, et al. Reliability analysis of railway freight car wheelset based on Birnbaunm-Saunders fatigue life distribution. Vibroeng Proc2020; 35: 88–93.
24.
WangYWNiYQWangX.Real-time defect detection of high-speed train wheels by using Bayesian forecasting and dynamic model. Mech Syst Signal Process2020; 139: 106654.
25.
ZengYSongDZhangW, et al. Physics-based data-driven interpretation and prediction of rolling contact fatigue damage on high-speed train wheels. Wear2021; 484–485: 203993.
26.
LinZLuZYongX, et al. Optimization of metro wheel reprofiling based on cluster analysis. Proc IMechE, Part F: J Rail and Rapid Transit2022; 236: 997–1007.
27.
ZhuWXiaoXHuangZ, et al. Evaluating the wheelset health status of rail transit vehicles: synthesis of wear mechanism and data-driven analysis. J Transp Eng Part A Syst2020; 146: 1–18.
28.
LinJAsplundM.Comparison study of heavy haul locomotive wheels’ running surfaces wearing. Eksploat i Niezawodn2014; 16: 276–287.
29.
BevanAMolyneux-BerryPMillsS, et al. Optimisation of wheelset maintenance using whole-system cost modelling. Proc IMechE, Part F: J Rail and Rapid Transit2013; 227: 594–608.
30.
AndradeARStowJ.Statistical modelling of wear and damage trajectories of railway wheelsets. Qual Reliab Eng Int2016; 32: 2909–2923.
31.
FreitasMAColosimoEAdos SantosTR, et al. Reliability assessment using degradation models: Bayesian and classical approaches. Pesq Oper2010; 30: 195–219.
32.
YangCLétourneauS.Learning to predict train wheel failures. In: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, Chicago, IL, 21–24 August 2005, pp.516–525. ACM.
33.
FerreiraJCFreitasMAColosimoEA.Degradation data analysis for samples under unequal operating conditions: a case study on train wheels. J Appl Stat2012; 39: 2721–2739.
34.
EkbergAKaboEAnderssonH.An engineering model for prediction of rolling contact fatigue of railway wheels. Fatigue Fract Eng Mater Struct2002; 25: 899–909.
35.
PomboJAmbrósioJPereiraM, et al. Development of a wear prediction tool for steel railway wheels using three alternative wear functions. Wear2011; 271: 238–245.
36.
ArchardJF.Contact and rubbing of flat surfaces. J Appl Phys1953; 24: 981–988.
37.
KalkerJJ. A fast algorithm for the simplified theory of rolling contact. Veh Syst Dyn1982; 11(1): 1–3.
38.
JendelTBergM.Prediction of wheel profile wear: methodology and verification. Veh Syst Dyn2003; 37: 502–513.
39.
PengBIwnickiSShackletonP, et al. Comparison of wear models for simulation of railway wheel polygonization. Wear2019; 436–437: 203010.
40.
Hossein NiaSCasanuevaCStichelS. Prediction of RCF and wear evolution of iron-ore locomotive wheels. Wear2015; 338–339: 62–72.
41.
GałeckiABurzykowskiT.Fitting linear mixed-effects models: the lmer() function. J Stat Softw2013; 67: 303–326.
42.
FoxJWeisbergS.Multivariate linear model. Thousand Oaks: SAGE Publications, 2019, pp.499–536.
43.
FoxJWeisbergS.An R companion to applied regression. Thousand Oaks: Sage Publications, 2011.
44.
BatesDMächlerMBolkerB, et al. Fitting linear mixed-effects models using lme4. arXiv Prepr arXiv14065823 2014Jun23.
45.
PinheiroJBatesDDebRoyS, et al. Nonlinear mixed-effects models. R Packag version2012; 3: 1–89.
46.
MontgomeryDCRungerGC. Applied statistics and probability for engineers. John wiley & sons, 2019.
47.
MüllerSScealyJLWelshAH.Model selection in linear mixed models. Stat Sci2013; 28: 135–167.
48.
CostaMAAlvesTPAndradeAR, et al. A hybrid maintenance approach for key components of the train bogie to optimize fleet availability. Eng Fail Anal2024; 165: 108815.
