The research is aimed at developing a predictive model of the risks in railroad transport when using diagnostics of the axle boxes of freight wagons, which will allow assessing traffic safety during the operation of freight wagons. To develop a predictive model of the risks in railroad transport, the approaches of the probability theory were used. The constructed predictive model of the risks makes it possible to assess traffic safety during freight transportations; to determine further measures to reduce them; to evaluate the diagnostic method. The authors developed a predictive model of the risks in railroad transport when using the diagnostic methods of freight wagons’ axle boxes during maintenance and repair of axle box. This model includes the probability of failure-free operation of the axle boxes of freight wagons, the probability of identifying of the technical condition of the axle boxes of freight wagons, the significance of the diagnostic parameter and its relative assessment after appropriate maintenance, repair or technical control.
ReidemeisterOGShykunovОA. Sensitivity of stresses to the forces acting on the cast parts of freight-carbogie. Sci Transport Progress2018; 4(76): 125–133. DOI: 10.15802/stp2018/14118.
2.
KovalchukVSysynMGerberU, et al.Experimental investigation of the influence of train velocity and travel direction on the dynamic behavior of stiff common crossings. Facta Univesitatis Series Mechanical Eng2019; 17(3): 345–356. DOI: 10.22190/FUME190514042K.
3.
ShvetsАО. Dynamic indicators influencing design solution for modernization of the freight rolling stock. FME Transactions2021; 49(3): 673–683. DOI: 10.5937/fme2103673S.
4.
ReidemeisterAGKalashnikVAShikunovAA. Modernization as a way to improve the use of universal cars. Sci Transport Progress2016; 2(62): 148–156.
5.
MuradianLAYuSVPodosenovDO. Improving the reliability of freight wagons with the use of new manufacturing technologies and regeneration of working surfaces. Electromagnetic Compatibility Safety in Railway Transport2016; 11: 49–54. DOI: 10.15802/ecsrt2016/91337.
6.
MuradianLAYuSV. Question of plans at trials reliability of mechanical systems. Collection Scientific Works Ukrainian State University Railway Transport2015; 157: 119–128.
7.
MuradianLAYuSV. Automatic identification of separate parts of vehicle with implementation of new concepts of maintenance and repair implementation. Information Control Systems for Railway Transport2017; 4: 44–50.
8.
MuradianLABaranovskyDN. Determination of parameters of operational reliability of freight cars in the system of maintenance and repair. Railway Transport of Ukraine2016; 1–2: 35–40.
9.
ShaposhnykVY. Human factor influence on performing technical maintenance and repair of freight cars. Sci Transport Progress2018; 6(78): 165–175. DOI: 10.15802/stp2018/154031.
10.
PengZhLuYuMillerA, et al.Risk assessment of railway transportation systems using timed fault trees. Quality Reliability Eng2016; 32: 181–194.
11.
BartlettLMDuS. New progressive variable ordering for binary decision diagram analysis of fault trees. Quality Reliability Eng International2005; 21(4): 413–425.
12.
MoYZhongFLiuH, et al.Efficient ordering heuristics in binary decision diagram-based fault tree analysis. Quality Reliability Eng International2013; 29(3): 307–315.
13.
HuangH-ZZhangHLiY. A new ordering method of basic events in fault tree analysis. Quality Reliability Eng International2012; 28(3): 297–305.
14.
LeitnerB. A general model for railway systems risk assessment with the use of railway accident scenarios analysis. Procedia Eng2017; 187: 150–159.
15.
Hadj-MabroukH. Contribution of artificial intelligence to risk assessment of railway accidents. Urban Rail Transit2019; 5(2): 104–122.
16.
Figueres-EstebanMHughesPVan GulijkC. Data for risk analysis: the future of safe railways. In: XII Congreso de ingeniería del transporte. Valencia, España: Editorial Universitat Politècnica de València, 2016, pp. 347–353.
17.
SmoczyńskiPKadzińskiA. Introduction to the risk management in the maintenance of railway tracks. J Mechanical Transport Engineering2016; 68(4): 65–80.
18.
Commission Implementing Regulation (EU). No. 402/2013 of 30 April 2013 on the common safety method for risk evaluation and assessment and repealing Regulation (EC). Europ: Commission Implementing Regulation (EU). No. 352/2009.
19.
StažnikABabićDBajorI. Identification and analysis of risks in transport chains. J Applied Eng Sci2017; 151(414): 61–70.
20.
JamshidiAFaghih-RoohiSHajizadehS, et al.A big data analysis approach for rail failure risk assessment. Risk Analysis2017; 37(8): 1495–1507.
21.
GandomiAHaiderM. Beyond the hype: big data concepts, methods, and analytics. International J Information Management2015; 35(2): 137–144.
22.
LingaitisLPMjamlinSBaranovskyD, et al.Experimental investigations on operational reliability of diesel locomotyves engines. Eksploatacja i Niezawodnosc – Maintenance and Reliability2012; 14(1): 5–10.
23.
OzMAKaymakcıOTKoyunA. A safety related perspective for the power supply systems in railway industry. Eksploatacja i Niezawodnosc – Maintenance and Reliability2017; 19(1): 114–120.
24.
LicciardelloRBaldassarraAVitaliP, et al.Limits and opportunities of risk analysis application in railway systems. Transactions on Built Environment2013; 134: 133–144.
25.
GrenčíkJPoprockýRGallikováJ, et al.Use of risk assessment methods in maintenance for more reliable rolling stock operation. MATEC Web of Conferences2018; 157: 04002.
26.
AbdelazizBEl-Koursi El-MCherkaouiA, et al.A framework for risk management in railway sector: application to road-rail level crossings. The Open Transportation J2011; 5: 34–44.
27.
JohansenS. Estimation and hypothesis testing of cointegration vectors in the presence of linear trend. Econometrica1991; 59: 1551–1580.
28.
MuradyanLAPitsenkoIVYuSV. Mathematical model of risks in railway transport during diagnostics of axle boxes of freight cars. Sci Transport Progress2021; 1(91): 94–103. DOI: 10.15802/stp2021/230223.
