Selection of a Decision Model for Rolling Stock Maintenance Scheduling

Asekun, Olabanji Olumuyiwa; Fourie, Cornelius Jacobus

doi:10.7166/26-1-1068

Cited by 11 publications

(10 citation statements)

References 25 publications

(23 reference statements)

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“…Predictive maintenance scheduling table for railways rolling stock will be designed to be compared with the previous one [12] designed for motor coach type 5M2A of Metrorail fleet which is a subsidiary of the Passenger Rail Service in South Africa (PRASA) at the Salt River Depot in Cape Town [5]. Failure data consisting of number of failure, time between failures, and cumulative failures for 5M2A motor coach component was obtained from the Fleet Maintenance…”

Section: Resultsmentioning

confidence: 99%

“…Management System (FMMS) in the period between 2003 and 2014 and this data is listed in [5]. According to reliability trend test carried out using both Laplace and Lewis Robinson trend test [21] [22], the components reliability has a degraded trend and is represented by Power Law NHPP and the parameters are estimated using least square method and their values are shown in Table 1 [5]…”

Section: Resultsmentioning

confidence: 99%

“…A multi-objective model to optimize the inspection and maintenance procedures with respect to both economic and safety objectives during railway track inspection have been proposed [4]. Railways rolling stocks have been proven to be repairable systems represented by non homogeneous data [5] where Rate of Occurrence of Failures (ROCOF) has a trend and can be predicted by Power Law Non Homogeneous Poisson Process (NHPP) [6]. Previous studies have used Power law NHPP to predict (ROCOF) in railways, for example Bayesian Neural Network (BNN) has been used for predicting failures in underground trains [7] and predicting the stochastic degradation process of metro rails to design maintenance activities scheduling [8].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Breeding Particle Swarm Optimization for Railways Rolling Stock Preventive Maintenance Scheduling

Aboueldah¹,

Farag²

2021

AJOR

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The goal of railway rolling stock maintenance and replacement approaches is to reduce overall cost while increasing reliability which is multi objective optimization problem and a proper predictive maintenance scheduling table should be adequately designed. We propose Breeding Particle Swarm Optimization (BPSO) model based on the concepts of Breeding Swarm and Genetic Algorithm (GA) operators to design this table. The practical experiment shows that our model reduces cost while increasing reliability compared to other models previously utilized.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Breeding Particle Swarm Optimization for Railways Rolling Stock Preventive Maintenance Scheduling

Aboueldah¹,

Farag²

2021

AJOR

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“…The components of rolling stock must be kept under adequate supervised maintenance in order to deliver a safe and reliable service. 6 The RS system cannot function reliably unless the existing maintenance crew and facilities for the available trainset are specified. 4 Railway failure prediction is challenging due to a lack of real-time database availability, which lowers performance targets.…”

Section: Existing Methods Adopted In Rolling Stock Concerning Mainten...mentioning

confidence: 99%

Model development synchronized with data mining for rolling stock maintenance strategy

Prabhakaran¹,

Anandakumar²,

Priyanka³

et al. 2022

Advances in Mechanical Engineering

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Indian metro system is comprised of many functional units for enabling smooth run of trains namely tracks, rolling stock, signaling and communication, operations and control center, projects, and design. Among those functional units, Rolling Stock (RS) is an integral part of any rapid transit system and also the most critical space if proper maintenance measures are neglected. The rolling stock department comprises interdisciplinary teams working together to ensure efficient delivery of trains on a service run. The trainsets of Metro Rail Networks are often subjected to both periodic and corrective maintenance based on service requirements. The maintenance schedule of the trainsets is monitored through a wireless communication mode. The train operator is responsible for alerting the nodal person regarding subsequent correspondence in the event of any emergency maintenance necessity. Therefore, this paper concentrates on the development of an IoT-based automatized maintenance prioritizing platform based on the incorrect operational sequence number that pops up in the operator’s cabin. A mathematical model is synchronized with the alert triggering signal from the field to categorize hierarchical decision-making on preventative and corrective maintenance. Simultaneously, a Genetic Algorithm is implemented to analyze the adopted combinations of maintenance say M1, M2, and M3 to identify the model that produced precise results. The test results reveal that the M3 model, which includes both corrective and preventative maintenance exhibits higher efficiency with a probability of 0.92%–0.98%. In addition, the combined maintenance prioritization system M3 offers the quickest analyzing time in the cloud computing platform (0.18 s) and the highest transaction performance on real-time datasets.

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“…Extensive research is ongoing with regard to how maintenance can be performed in a cost-effective manner. 2…”

Section: Introductionmentioning

confidence: 99%

Optimizing the re-profiling policy regarding metropolitan train wheels based on a semi-Markov decision process

Jiang

Banjević

Mingcheng

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part O:

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In this article, we present a maintenance model for metropolitan train wheels subjected to diameter or flange thickness overruns that includes condition monitoring with periodic inspection. We present a dynamic ([Formula: see text], [Formula: see text]) policy based on condition monitoring information, where [Formula: see text] is the wheel flange thickness threshold that triggers preventive re-profiling and [Formula: see text] is the recovery value for the wheel flange thickness after preventive re-profiling. The problem is modelled as a semi-Markov decision process that considers wear in terms of the diameter and flange thickness simultaneously. The problem is formulated in a two-dimensional state space; this space is defined as a combination of the diameter state and the flange thickness state. The model also considers imperfect wheel maintenance. The model’s objective is to minimize the maintenance cost per unit time that is expected in the long run. We apply a policy-iteration algorithm as the computational approach to determine the optimal re-profiling policy and use an example to demonstrate the method’s effectiveness.

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Selection of a Decision Model for Rolling Stock Maintenance Scheduling

Cited by 11 publications

References 25 publications

Breeding Particle Swarm Optimization for Railways Rolling Stock Preventive Maintenance Scheduling

Breeding Particle Swarm Optimization for Railways Rolling Stock Preventive Maintenance Scheduling

Model development synchronized with data mining for rolling stock maintenance strategy

Optimizing the re-profiling policy regarding metropolitan train wheels based on a semi-Markov decision process

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