Abstract
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.
Highlights
Railway track is one of the most critical parts in railway system
Results of the Systematic Literature Review section summarizes the methods “inspection”, and models used in the identified publications to answer theThis most frequent words “geometry”, “defects”, “prediction”, and “degradation”
Various classical and advanced data-driven algorithms applied in predictive maintenance of railway track are discussed
Summary
Track-caused accidents has consistently constituted 30–40% of total accidents for the past decade in America [1]. Huge axle loads and varying environmental conditions even small flaws in railway track may develop into severe damage [2,3]. To avoid disruption in rail network, railway tracks need to be maintained regularly and monitored for unusual degradation. The railway industry spends a large amount of money on maintenance and renewal projects. The annual maintenance expenditure of British railway infrastructure was more than £1 billion in 2015; almost two-thirds of the Network Rail organization’s employees engaged in maintenance work [4]. In the United States of America, over half of the railway maintenance costs are related to track [5]
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