The security of a train becomes a more critical issue as the train’s speed and the complexity of the railway conditions increases. It is especially true when the train runs on a curved radius rail when the lateral force between the train and the rail is less stable. The rail’s side grinding is a significant problem that affects the train’s safety, especially when the train passes through small radial sections in mountainous areas. The intelligent rail lubrication system is critical to enhancing rails’ safety and efficiency and reducing grease pollution along rail lines. This system is modeled with a force analysis of train curve motion and numerical simulation of wear power. The lubrication system is constructed with hardware and software. Based on fuzzy group analysis, this system and the adaptive Proportional Integration Differential (PID) controller is presented to improve the lubricative effects. The system test results show that the quality of lubrication control using this system is efficacious; the control convergence is more reliable than the conventional PID controller.
TaoG., RenD., WangL., WenZ. and JinX., Online prediction model for wheel wear considering track flexibility [J], Multibody System Dynamics44(3) (2018), 313–334.
2.
LiY., RenZ., EnblomR., StichelS. and LiG., Wheel wear prediction on a high-speed train in China [J], Vehicle System Dynamics (2019), 1–20.
3.
KaewunruenS., SussmanJ.M. and EinsteinH.H., Strategic framework to achieve carbon-efficient construction and maintenance of railway infrastructure systems [J], Frontiers in Environmental Science3 (2015), 6.
KandevaM., IvanovaB., AssenovaE. and VenclA., Influence of additives and selective transfer on wear reduction in the lubricated contact. In: Proceedings of the International Conference on Materials, Tribology, Recycling–MATRIB: 2014; (2014), 26–28.06.
6.
EnblomR., Deterioration mechanisms in the wheel–rail interface with focus on wear prediction: a literature review [J], Vehicle System Dynamics47(6) (2009), 661–700.
7.
JinX., WenZ., WangK. and ZhangW., Effect of a scratch on curved rail on initiation and evolution of rail corrugation [J], Tribology International37(5) (2004), 385–394.
8.
AlarcónG.I., BurgelmanN., MezaJ.M., ToroA. and LiZ., The influence of rail lubrication on energy dissipation in the wheel/rail contact: a comparison of simulation results with field measurements [J], Wear330 (2015), 533–539.
9.
KumarS. and KumarS.R., Intelligent on-board rail lubrication system for curved and tangent track. In.: Google Patents; (1990).
10.
KossovV., LuninA., SpirovA., PaninY., IvaškovskaN. and NikolajevsA., The technology of rail lubrication by the hauling locomotive in train formation [J], Procedia Computer Science149 (2019), 331–335.
11.
ZboinskiK. and DuszaM., Bifurcation approach to the influence of rolling radius modelling and rail inclination on the stability of railway vehicles in a curved track [J], Vehicle System Dynamics46(S1) (2008), 1023–1037.
12.
SpiryaginM., LeeK.S., YooH.H., KashuraO. and PopovS., Numerical calculation of temperature in the wheel–rail flange contact and implications for lubricant choice [J], Wear268(2) (2010), 287–293.
13.
AbbasiS., OlofssonU., ZhuY. and SellgrenU., Pin-on-disc study of the effects of railway friction modifiers on airborne wear particles from wheel–rail contacts [J], Tribology International60 (2013), 136–139.
14.
CannonD., EdelK., GrassieS. and SawleyK., Rail defects: an overview [J], Fatigue & Fracture of Engineering Materials & Structures26(10) (2003), 865–886.
15.
NielsenJ.C., LundénR., JohanssonA. and VernerssonT., Train-track interaction and mechanisms of irregular wear on wheel and rail surfaces [J], Vehicle System Dynamics40(3) (2003), 3–54.
16.
BakhshiA. and YazdiM., Analysis and modeling of rail maintenance costs [J], Management Science Letters2(1) (2012), 87–92.
17.
NagendrammaP. and KaulS., Development of ecofriendly/biodegradable lubricants: An overview [J], Renewable and Sustainable Energy Reviews16(1) (2012), 764–774.
18.
SeisingR., Lotfi Aliasker Zadeh (1921–2017)–his life and work from the perspective of a historian of science [J], Fuzzy Sets and Systems331 (2018), 3–11.
CordónO., A historical review of evolutionary learning methods for Mamdani-type fuzzy rule-based systems: Designing interpretable genetic fuzzy systems [J], International Journal of Approximate Reasoning52(6) (2011), 894–913.
21.
AlbertosP. and SalaA., Fuzzy logic controllers. Advantages and drawbacks. In: VIII International Congress of Automatic Control: 1998; (1998), 833–844.
22.
LinkensD.A. and NyongesaH.O., Learning systems in intelligent control: an appraisal of fuzzy, neural and genetic algorithm control applications [J], IEE Proceedings-Control Theory and Applications143(4) (1996), 367–386.
23.
WangL.-X., Stable adaptive fuzzy control of nonlinear systems [J], IEEE Transactions on Fuzzy Systems1(2) (1993), 146–155.
24.
LarsenH.L. and YagerR.R., A framework for fuzzy recognition technology [J], IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)30(1) (2000), 65–76.
25.
HongD.H. and ChoiC.-H., Multicriteria fuzzy decision-making problems based on vague set theory [J], Fuzzy Sets and systems114(1) (2000), 103–113.
26.
TimmH., BorgeltC., DöringC. and KruseR., An extension to possibilistic fuzzy cluster analysis [J], Fuzzy Sets and Systems147(1) (2004), 3–16.
27.
ZadehL.A., Fuzzy systems theory: a framework for the analysis of humanistic systems. In: Systems Methodology in Social Science Research. Sringer; 1982, 25–41.
28.
KarS., DasS. and GhoshP.K., Applications of neuro fuzzy systems: A brief review and future outline [J], Applied Soft Computing15 (2014), 243–259.
29.
González-GilA., PalacinR. and BattyP., Sustainable urban rail systems: Strategies and technologies for optimal management of regenerative braking energy [J], Energy Conversion and Management75 (2013), 374–388.
30.
LiuH., LiuL. and LinQ., Fuzzy failure mode and effects analysis using fuzzy evidential reasoning and belief rule-based methodology [J], IEEE Transactions on Reliability62(1) (2013), 23–36.
31.
VichuzhaninV., Realization of a fuzzy controller with fuzzy dynamic correction [J], Central European Journal of Engineering2(3) (2012), 392–398.
32.
HuB., MannG.K. and GosineR.G., A systematic study of fuzzy PID controllers-function-based evaluation approach [J], IEEE Transactions on Fuzzy Systems9(5) (2001), 699–712.
33.
ZhaoY., YangB., PengJ. and ZhangW., Drawing and Application of Goodman-Smith Diagram for the Design of Railway Vehicle Fatigue Reliability [J], China Railway Science26(6) (2005), 6–12.
