Position computation models for high-speed train based on support vector machine approach

Chen, Dewang; Wang, Limin; Li, Lingxi

doi:10.1016/j.asoc.2015.01.017

Cited by 38 publications

(14 citation statements)

References 27 publications

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“…Other tools may contribute to the development of a new model for optimization of bus replacement, such as fuzzy logic and support vector machine (Tsoukalas et al 1997;Yager, Zadeh 1992;Campello et al 2001;Couellan et al 2015;Chen et al 2015;Pooyan et al 2015).…”

Section: State-of-the-art Analysismentioning

confidence: 99%

Reserve Fleet Indexed to Exogenous Cost Variables

et al. 2019

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Identifying the optimal time to replace a passenger bus in a buses fleet has implications on the size of the reserve fleet. Such calculations rest on endogenous and exogenous economic variables: the former include operating and maintenance costs and bus depreciation; the latter include market imponderables such as the inflation and real discount rates, as well as energy costs, particularly fuel. The authors have created models for the withdrawal/replacement of buses using endogenous economic variables. The models include standard econometric models reflecting the influence of maintenance policies, especially Condition Monitoring (CM) or predictive maintenance, and the size of the reserve fleet. The paper deals with exogenous economic variables, specifically the influence of the cost of money, the inflation and real discount rates rate and the cost of fuel. Both variables fluctuate over time. The paper proposes analytical models for determining the influence of those variables on the withdrawal time and the size of the reserve fleet. It then comprehensively summarizes the variables in a global model, showing its relevance to the dimensioning of the reserve fleet and the withdrawal time.

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Section: State-of-the-art Analysismentioning

confidence: 99%

Reserve Fleet Indexed to Exogenous Cost Variables

et al. 2019

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“…It has been successfully applied in several areas, such as the categorization of texts, complex systems optimization models for high-speed trains, image analysis, bioinformatics, maintenance prediction and process optimization. Some cases of SVM are reported in [39][40][41][42].…”

Section: State Of the Art Maintenance Managementmentioning

confidence: 99%

Economic Life Cycle <i>versus</i> Lifespan – A Case Study of an Urban Bus Fleet

Raposo¹,

Farinha²,

Fonseca³

et al. 2019

EAS

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The maintenance policy and the quality of service throughout the bus's life cycle can be measured through their costs along time that, when evaluated using Lifespan or Economic Life Cycle methods, allow to determine the renewal or the replacement time. The paper discusses these two models, using real data from an urban bus fleet company. The maths that supports the models are presented. They are considered the functioning and maintenance costs, and also the apparent rate. The Life Cycle Cost of an urban transport bus is strongly dependent on the policy and quality of its maintenance, from which it depends on its reliability and availability. The final result is reflected on its Life Cycle Cost, that can be evaluated through the Lifespan or the Economic Life Cycle methods. Other aspects that can be considered are the fuel costs and the type of terrain, because they are intrinsically interrelated and have a strong effect on costs, namely because they imply strong variation in the bus's consumption and in their maintenance costs. As the company considered in the case study has a poor maintenance policy, it makes the analysis challenging, making difficult to compare the economic life cycle with the lifespan method in this situation. However, the results and conclusions that are taken from them are obvious, what demonstrates the models' utility and robustness.

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“…Other tools may contribute to the development of a new model for the optimization of bus replacement, such as Fuzzy Logic and Support Vector Machine (SVM) [14,17,18,41,44,48,53]. For predictive maintenance, specifically using oil analysis, several mathematical models are appropriate [2,12,13,22,46].…”

Section: State Of the Artmentioning

confidence: 99%

An integrated econometric model for bus replacement and determination of reserve fleet size based on predictive maintenance

Raposo¹,

Farinha²,

Ferreira³

et al. 2017

EiN

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Position computation models for high-speed train based on support vector machine approach

Cited by 38 publications

References 27 publications

Reserve Fleet Indexed to Exogenous Cost Variables

Reserve Fleet Indexed to Exogenous Cost Variables

Economic Life Cycle <i>versus</i> Lifespan – A Case Study of an Urban Bus Fleet

An integrated econometric model for bus replacement and determination of reserve fleet size based on predictive maintenance

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Position computation models for high-speed train based on support vector machine approach

Cited by 38 publications

References 27 publications

Reserve Fleet Indexed to Exogenous Cost Variables

Reserve Fleet Indexed to Exogenous Cost Variables

Economic Life Cycle &lt;i&gt;versus&lt;/i&gt; Lifespan – A Case Study of an Urban Bus Fleet

An integrated econometric model for bus replacement and determination of reserve fleet size based on predictive maintenance

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Economic Life Cycle <i>versus</i> Lifespan – A Case Study of an Urban Bus Fleet