Abstract
The rapid growth of population density and people demand based on railway services increases day by day. Hence, the maintenance (i.e., degradation and aging) and the decision-making processes of rail wheels are troublesome. There has been an impact of degradation stage for a system such as a repair, replacement, and inspection time. In this paper, an adaptive neural-based fuzzy inference system (ANFIS) rail wheel maintenance model is proposed using the modified spider monkey algorithm, with the parametric update. As the design of the wheel maintenance strategy, the degradation stage increases with system aging and it can be minimized using repair. In this way, this article presents the development of system reliability that is dealing with the establishment of a maintenance strategy. A higher running cost rate reduces the main contribution of the reparation cycle using an optimum maintenance model, and the locomotive wheels are employed to demonstrate the efficiency of the proposed methodology. Under optimal inspection time, the impact of system failures such as aging and degradation is determined successfully. The proposed model can able to identify the precise time when rail tracks fail to minimize the maintenance cost/time. This ANFIS and MSMA can increase the efficiency of maintenance activities and decrease the cost of maintenance in the long term. Similarly, the results demonstrate that the proposed maintenance model is more flexible, and the capability of gauge value prediction using real and estimated values is accurate.
Similar content being viewed by others
References
Abualigah LMQ (2019) Feature selection and enhanced krill herd algorithm for text document clustering. Springer, Berlin, pp 1–165
Abualigah LMQ, Hanandeh ES (2015) Applying genetic algorithms to information retrieval using vector space model. Int J Comput Sci Eng Appl 5(1):19
Abualigah LM, Khader AT (2017) Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering. J Supercomput 73(11):4773–4795
Abualigah LM, Khader AT, Hanandeh ES, Gandomi AH (2017) A novel hybridization strategy for krill herd algorithm applied to clustering techniques. Appl Soft Comput 60:423–435
Abualigah LM, Khader AT, Hanandeh ES (2018a) Hybrid clustering analysis using improved krill herd algorithm. Appl Intell 48(11):4047–4071
Abualigah LM, Khader AT, Hanandeh ES (2018b) A combination of objective functions and hybrid Krill herd algorithm for text document clustering analysis. Eng Appl Artif Intell 73:111–125
Abualigah LM, Khader AT, Hanandeh ES (2018c) A new feature selection method to improve the document clustering using particle swarm optimization algorithm. J Comput Sci 25:456–466
Akhtar K, Shau S, Kumar A (2018) Phase transformations and numerical modelling in simulated HAZ of nano structured P91B steel for high temperature applications. Appl Nano Sci 8(7):1669–1685
Arcidiacono C (2018) A model of control valve for wagons equipped by k-blocks. Int J Adv Sci Eng Inf 8(1):285–290
Azizi F, Haghighi F (2018) Joint modelling of linear degradation and failure time data with masked causes of failure under simple step-stress test. J Stat Comput Simul 88(8):1603–1615
Chern S, Jeng-Haur C, Han H (2018) The micro-temperatures of the peaks and valleys of sliding rough surface. Appl Mech Mater 883:53–62
Cimen MA, Ararat O, Soylemez MT (2018) A new adaptive slip slide control system for railway vehicles. Mech Syst Signal Process 111:265–284
Faccoli M, Petrogali C, Ghidini A (2019) On mechanical properties of new railway wheel steels for desert environments and sand caused wheel damage mechanism. J Mater Eng Perform 28(5):2946–2953
Falamarzi A, Moridpour S, Nazem M, Hesami R (2018) Rail degradation prediction models for tram system: Melbourne case study. J Adv Transp. https://doi.org/10.1155/2018/6340504
Falamarzi A, Moridpour S, Nazem M, Cheraghi S (2018) Development of a random forests regression model to predict track degradation index: Melbourne case study. In: Australian transport research forum, p 12
Falamarzi A, Moridpour S, Nazem M, Hesami R (2019) Integration of genetic algorithm and support vector machine to predict rail track degradation. In: MATEC web of conferences. EDP Sciences, vol 259, p 02007
Karimpour M, Hitihamillage L, Elkhoury N, Moridpou S, Hesami R (2018) Fuzzy approach in rail track degradation prediction. J Adv Transp . https://doi.org/10.1155/2018/3096190
Khajehei H, Ahmadi A, Soleimanmeigouni I, Nissen A (2019) Allocation of effective maintenance limit for railway track geometry. Struct Infrastruct Eng 15:1–16
Kharanaghi MM, Briaud JL (2020) Large-scale direct shear test on railroad ballast. In: Geo-congress 2020: modeling, geomaterials, and site characterization. American Society of Civil Engineers, Reston, pp 123–131
Kishore PVV, Sasikala N, Prasad CR (2019) Localized region based active contours with a weakly supervised shape image for homogeneous video segmentation of train bogie parts in building an automated train rolling examination. Multimedia Tools Appl 78(11):14917–14946
Koohmishi M, Palassi M (2020) Degradation of railway ballast under compressive loads considering particles rearrangement. Int J Pavement Eng 21(2):157–169
Lee JS, Hwang SH, Choi IY, Choi Y (2020) Deterioration prediction of track geometry using periodic measurement data and incremental support vector regression model. J Transp Eng A Syst 146(1):04019057
Liu B, Liu J, Xie M (2018) A dynamic maintenance strategy for prognostics and health management of degrading system: applications in locomotive wheel sets. In: 2018 IEEE international conference on prognostics and health management (ICPHM), pp 1–5
Liu B, Lin J, Zhang L, Kumar U (2019) A dynamic prescriptive maintenance model considering system aging and degradation. IEEE Access 7:94931–94943
Manka A, Sitarz M (2016) Effects of a thermal load on the wheel or brake sub system: The thermal conicity of railway wheels. Proc Inst Off Mech Eng F J Rail Rapid Transit 230(1):193–205
Naeimi M, Li Z, Petrov RH, Sietsma J, Dollevoet R (2018) Development of a new downscale setup for wheel rail contact experiments under impact loading conditions. Exp Tech 42(1):1–17
Nami J, Fallah N (2018) Spatial prediction of wildfire probability in the hyrcanian eco-region using evidential belief function model and GIS. Int J Environ Sci Technol 15(2):373–384
Nguyen S, Seo T-I (2018) Establishing ANFIS and the use for predicting sliding control of active railway suspension systems subjected to uncertainties and disturbances. Int J Mach Learn Cybern 9(5):853–865
Pichlik Z (2018) Locomotive wheel slip control method based on an unscented kalman filter. IEEE Trans Veh Technol 67(7):5730–5739
Prakash G (2020) A Bayesian approach to degradation modeling and reliability assessment of rolling element bearing. Commun Stat Theory Methods. https://doi.org/10.1080/03610926.2020.1734826
Seo J-W, Jun H-K, Seok D (2018) Effect of friction modifier on rolling contact fatigue and wear of wheel and rail materials. Tribol Trans 61(1):19–30
Sharma A, Sharma A, Panigrahi BK, Kiran D, Kumar R (2016) Ageist spider monkey optimization algorithm. Swarm Evol Comput 28:58–77
Shi H, Wang J, Wu P, Song C, Teng W (2018) Field measurements of the evolution of wheel wear and vehicle dynamics for high speed trains. Veh Syst Dyn 56(8):1187–1206
Shrivastava JP, Sarkar PK, Kiran MV, Rajan V (2019) A numerical study on effects of friction induced thermal load for rail under varied wheel slip conditions. Simulation 95(4):351–362
Sundararaj V (2016) An efficient threshold prediction scheme for wavelet based ECG signal noise reduction using variable step size firefly algorithm. Int J Intell Eng Syst 9(3):117–126
Sundararaj V (2019) Optimal task assignment in mobile cloud computing by queue based ant-bee algorithm. Wirel Pers Commun 104(1):173–197
Sundararaj V, Muthukumar S, Kumar RS (2018) An optimal cluster formation based energy efficient dynamic scheduling hybrid MAC protocol for heavy traffic load in wireless sensor networks. Comput Secur 77:277–288
Sundararaj V, Anoop V, Dixit P, Arjaria A, Chourasia U, Bhambri P, MR R, Sundararaj R (2020) CCGPA-MPPT: Cauchy preferential crossover-based global pollination algorithm for MPPT in photovoltaic system. Prog Photovolt Res Appl 28(11):1128–1145
Teodoro I, Daniel R, Tiago A (2019) Fast simulation of railway pneumatic brake systems. Proc Inst Mech Eng F J Rail Rapid Transit 233(4):420–430
Vinu S (2019) Optimised denoising scheme via opposition-based self-adaptive learning PSO algorithm for wavelet-based ECG signal noise reduction. Int J Biomed Eng Technol 31(4):325
Walia MS, Esmaeili A, Vernersson R (2018) Thermo-mechanical capacity of wheel treads at stop braking: a parametric study. Int J Fatigue 113:407–415
Wu Su, Cheng S, Deng L (2018) Multi-sensor information fusion for remaining useful life prediction off machining tools by adaptive network based fuzzy inference system. Appl Soft Comput 68:13–23
Xu L, Zhai W, Chen Z (2018) On use of characteristic wavelengths of track irregularities to predict portions with deteriorated wheel or rail forces. Mech Syst Signal Process 104:264–278
Zhou D, Pan E, Zhang X, Zhang Y (2020) Dynamic model-based saddle-point approximation for reliability and reliability-based sensitivity analysis. Reliab Eng Syst Saf 201:106972
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Human and animal rights
This article does not contain any studies with human or animal subjects performed by any of the authors.
Informed consent
Informed consent was obtained from all individual participants included in the study.
Additional information
Communicated by V. Loia.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Umamaheswari, R., Chitra, S. & Kavitha, D. Reliability analysis and dynamic maintenance model based on fuzzy degradation approach. Soft Comput 25, 3577–3592 (2021). https://doi.org/10.1007/s00500-020-05388-4
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-020-05388-4