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

Jiang, Zhenan; Banjević, Dragan; Mingcheng, E; Li, Bing

doi:10.1177/1748006x17710816

Cited by 8 publications

(5 citation statements)

References 35 publications

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“…After each maintenance, a wheelset becomes normal (at least without visible damage or cracks), and a new maintenance cycle begins. 18,19 The longer the cumulative running mileage after maintenance, the greater the risk of wheel damage. 16,20…”

Section: Basic Conceptsmentioning

confidence: 99%

Influence of different railway lines on wheel damage of high-speed trains: Data-driven modelling and prediction

Zeng

Song

Zhang

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part O:

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Wheel damage of high-speed trains has received great attention since it is detrimental to safety and reliability and consumes huge maintenance costs. However, the mixed operation mode of electric multiple units (EMUs) in China induces great challenges in distinguishing the influence of different railway lines on wheel damage. This paper develops a data-driven approach to quantify the influence of different railway lines on wheel damage and further applies it to damage risk prediction. First, under several fundamental assumptions, linear discriminant analysis and the proposed data padding strategy are used to quantify the influence on wheel damage due to per unit mileage of running on each railway section. The result shows that the influence on wheel damage varies significantly for different railway sections. Then, a route-based predictor is defined to represent the cumulative influence of an EMU’s routes on its wheel damage. Further, a wheel damage prediction model is developed based on a support vector machine using the route-based predictor and three other predictors from the operational data. Validation and test results demonstrate that the actual damage rate is higher for wheelsets predicted with higher damage probabilities. The proposed methodology has the potential to be applied to the risk-based operation and maintenance of high-speed trains.

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Section: Basic Conceptsmentioning

confidence: 99%

Influence of different railway lines on wheel damage of high-speed trains: Data-driven modelling and prediction

Zeng

Song

Zhang

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part O:

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“…Jiang et al 25 present a bidimensional wear model considering the wheel diameter and flange thickness, where the maintenance policy allows the threshold for re‐profiling and the recovery value to be optimized conditioned on the wear state of the wheel. A semi‐Markov decision process (SMDP) framework is proposed with the objective of minimizing the long‐run expected average maintenance cost per unit time while considering the effects of imperfect maintenance.…”

Section: Markov Decision Process (Mdp)mentioning

confidence: 99%

A data‐driven maintenance policy for railway wheelset based on survival analysis and Markov decision process

Costa

Braga

Andrade

2020

Quality & Reliability Eng

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Wheelsets absorb a significant part of the maintenance budget of any train operating company. Although wheel wear has been an extensively discussed topic in the literature, wear rates are very rarely characterized by using degradation data in a real‐world case study aimed at identifying optimal maintenance policies including both degradation and recovery modeling. Furthermore, wheel defects, which impose an additional challenge to the modeling of the lifecycle of the wheels, are usually considered separately in the literature. In this study, conducted at a Portuguese train operating company, 17 years of inspection data are used to estimate wheel wear rates and survival curves, which are further incorporated into a Markov decision process (MDP) model. A bidimensional framework considering discrete intervals of wheel diameter along with a quantitative variable (kilometers since last turning/renewal) is used to represent the possible wheel states, while the probability of a defect interfering with the wheel maintenance schedule is modeled by contemplating survival curves derived from a Cox proportional‐hazards model. Optimal results in terms of minimal cost policy are discussed in the context of the MDP, but a more realistic and easy‐to‐implement policy fixing one of the parameters is compared with the optimal policy. Results showed that in practice train operating companies might benefit from using the easy‐to‐implement policy, which has an associated long‐run average cost only about 1% higher than the one suggested by the optimal decision map.

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“…The railway wheelset is one of the most important components of modern train systems, since it allows the train to curve, keep it on track, while ensuring the passenger comfort and avoiding train derailment. However, it is also one of the top three train components most affected by wear, 1 and worse than that, with serious consequences due to damage. This causes serious implications for the passenger safety and comfort, as well as for the wheelset life cycle itself.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Sharma et al 4 provide an MDP optimal maintenance policy for the railway track, in which the wear evolution is modelled (and railway track states are set) based on quality levels of the ability that the railroad track can perform its function and the effect of geo-defects, that is, on qualitative terms. A very recent reference is a notable exception: Jiang et al 1 optimize the re-profiling policy for train wheels using a semi-MDP, considering wear in terms of the diameter and flange thickness simultaneously. Their problem is formulated in a twodimensional state space; this space is defined as a combination of the diameter state and the flange thickness state.…”

Section: Introductionmentioning

confidence: 99%

Optimizing maintenance decisions in railway wheelsets: A Markov decision process approach

Braga

Andrade

2018

Proceedings of the Institution of Mechanical Engineers, Part O:

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This article models the decision problem of maintaining railway wheelsets as a Markov decision process, with the aim to provide a way to support condition-based maintenance for railway wheelsets. A discussion on the role of the railway wheelsets is provided, as well as some background on the technical standards that guide maintenance decisions. A practical example is explored with the estimation of Markov transition matrices for different condition states that depend on the wheelset diameter, its mileage since last turning action (or renewal) and the damage occurrence. Bearing in mind all the possible maintenance actions, an optimal strategy is achieved, providing a map of best actions depending on the current state of the wheelset.

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

Cited by 8 publications

References 35 publications

Influence of different railway lines on wheel damage of high-speed trains: Data-driven modelling and prediction

Influence of different railway lines on wheel damage of high-speed trains: Data-driven modelling and prediction

A data‐driven maintenance policy for railway wheelset based on survival analysis and Markov decision process

Optimizing maintenance decisions in railway wheelsets: A Markov decision process approach

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