A review of rail track degradation prediction models

Falamarzi, Amir; Moridpour, Sara; Nazem, Majidreza

doi:10.1080/14488353.2019.1667710

Cited by 33 publications

(14 citation statements)

References 41 publications

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“…Several authors [7][8][9] provide a comprehensive review of rail-degradation prediction models and classify degradation models as mechanistic models, statistic models, mechanical-empiric models and artificial-intelligence models. Mechanistic models are based on the knowledge and understanding of the behavior of the mechanical components.…”

Section: A Review Of Rail Track-degradation Modelsmentioning

confidence: 99%

Prediction of Railway Track Condition for Preventive Maintenance by Using a Data-Driven Approach

Vale

Simões

2022

Infrastructures

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For scheduling track maintenance, infrastructure managers perform inspections to assess the track condition. When the irregularities are higher than the threshold limits, a track has a defect that should be corrected to avoid future failures or traffic disruption. Scheduling maintenance actions contribute to reliability and availability but demand the prediction of the evolution of track degradation. In recent years, several degradation models have been defined to forecast geometrical evolution over time and/or tonnage, mainly for heavy rail systems. Nevertheless, most of those models have limitations when dealing with measured data collected in different time intervals as happens in reality. To overcome this problem, a data-driven model based on the logistic binary function is presented and validated with real inspection measurements. The results prove that the model has a 91.1% success rate, an excellent discrimination ability, and a high sensitivity, classifying correctly 84.1% of inspections in need of maintenance. The model also has high specificity as it classifies 94.5% of inspections with no demand of maintenance action. The model is easy to implement, which is also an advantage for the track asset management with guaranty of excellent sensitivity and discrimination.

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Section: A Review Of Rail Track-degradation Modelsmentioning

confidence: 99%

Prediction of Railway Track Condition for Preventive Maintenance by Using a Data-Driven Approach

Vale

Simões

2022

Infrastructures

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“…Data-driven methods discover viable feature sets and decision criteria from observed data. These methods include statistical models and machine learning models [14]. The primary difference between these two types lies in the main goal of the analysis.…”

Section: High Noisementioning

confidence: 99%

“…For example, in Soleimanmeigouni [15], a survey of track geometry degradation and maintenance models was conducted. A survey of track degradation prediction models based on mechanical models, statistical models and artificial intelligence models was provided in Reference [14]. Sol-Sánchez [16] conducted a literature review focusing on the effectiveness of the major conventional techniques and materials for track design and maintenance, as well as innovative solutions being developed to reduce track degradation.…”

Section: High Noisementioning

confidence: 99%

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Systematic Literature Review on Data-Driven Models for Predictive Maintenance of Railway Track: Implications in Geotechnical Engineering

et al. 2020

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Conventional planning of maintenance and renewal work for railway track is based on heuristics and simple scheduling. The railway industry is now collecting a large amount of data with the fast-paced development of sensor technologies. These data sets carry information about the conditions of various components in railway track. Since just before the beginning of the 21st century, data-driven models have been used in the predictive maintenance of railway track. This study presents a systematic literature review of data-driven models applied in the predictive maintenance of railway track. A taxonomy to classify the existing literature based on types of models and types of applications is provided. It is found that applying the deep learning methods, unsupervised methods, and ensemble methods are the new trends for predictive maintenance of railway track. Rail geometry irregularity, rail head defect, and missing rail components detection were the top three most commonly considered issues within the application of data-driven models. Prediction of rail breaks has received increasing attention in the last four years. Among these data-driven model applications, the collected data types are the most critical factors which affect selecting suitable models. Finally, this study discusses upcoming challenges in the predictive maintenance of railway track.

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“…Statistical models which can be categorized into three types as deterministic, probabilistic, and stochastic have been used in previous research for the estimation of rail wear [9]. Costello et al [10] developed a stochastic rail wear model by using the Markov process for rail wear simulation by means of 10 years of rail wear data from New Zealand's railroad database.…”

Section: Introductionmentioning

confidence: 99%

Effects of Traffic Loads and Track Parameters on Rail Wear: A Case Study for Yenikapi–Ataturk Airport Light Rail Transit Line

Sönmez

Öztürk

2020

Urban Rail Transit

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The aim of this study is to investigate the effects of traffic loads and track parameters, including track curvature, superelevation, and train speed, on vertical and lateral rail wear. The Yenikapi–Ataturk Airport Light Rail Transit (LRT) line in Istanbul was selected as a case study, and rail wear measurements were carried out accordingly. Passenger counts were performed in all wagons of the train on different days and time intervals to calculate the number of passengers carried in track sections between stations regarding traffic loads on the LRT line. Values of traffic load, track curvature, superelevation, and speed were determined for each kilometer where measurements of rail wear were conducted. A multiple linear regression analysis (MLRA) method was used to identify effective parameters on rail wear. Independent variables in MLRA for both vertical and lateral wear include traffic load, track curvature, superelevation, and train speed. The dependent variables in MLRA for vertical and lateral wear are the amount of vertical and lateral wear, respectively. The correlation matrix of the dependent and independent variables was analyzed before performing MLRA. Multicollinearity tests and cross-validation analyses were conducted. According to the results of MLRA for vertical and lateral wear, the obtained coefficients of determination indicate that a high proportion of variance in the dependent variables can be explained by the independent variables. Traffic load has a statistically significant effect on the amount of vertical and lateral rail wear. However, track curvature, superelevation, and train speed do not have a statistically significant effect on the amount of vertical or lateral rail wear.

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A review of rail track degradation prediction models

Cited by 33 publications

References 41 publications

Prediction of Railway Track Condition for Preventive Maintenance by Using a Data-Driven Approach

Prediction of Railway Track Condition for Preventive Maintenance by Using a Data-Driven Approach

Systematic Literature Review on Data-Driven Models for Predictive Maintenance of Railway Track: Implications in Geotechnical Engineering

Effects of Traffic Loads and Track Parameters on Rail Wear: A Case Study for Yenikapi–Ataturk Airport Light Rail Transit Line

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