Rail Flaw Detection Technologies for Safer, Reliable Transportation: A Review

Alahakoon, Sanath; Sun, Yan Quan; Spiryagin, Maksym; Cole, Colin

doi:10.1115/1.4037295

Cited by 61 publications

(26 citation statements)

References 87 publications

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“…Review papers considering contact, near-contact and non-contact methods (Papaelias et al 2008) and more recently by team member, Alahakoon et al (2018), on non-destructive evaluation of rails presents a thorough state-of-the-art in defect technology. A summary of nondestructive techniques applicable to rail foot flaw detection from that time and recent advancements are shown in Table 1.…”

Section: Existing Detection Techniquesmentioning

confidence: 99%

Modelling Rail Thermal Differentials Due to Bending and Defects

et al. 2021

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Rail foot flaws have the potential to cause broken rails that can lead to derailment. This is not only an extremely costly issue for a rail operator in terms of damage to rolling stock, but has significant flow-on effects for network downtime and a safe working environment. In Australia, heavy haul operators run up to 42.5 t axle loads with trains in excess of 200 wagons and these long trains produce very large cyclic rail stresses. The early detection of foot flaws before a broken rail occurs is of high importance and there are currently no proven techniques for detecting rail foot flaws on trains at normal running speeds. This paper shall focus on the potential use of thermography as a detection technique and begin investigating the components of heat transfer in the rail to determine the viability of thermography for detecting rail foot flaws. The paper commences with an introduction to the sources of heat generation in the rail and modelling approaches for the effects of bending, natural environmental factors and transverse defects. It concludes with two theoretical case studies on heat generated due to these sources and discusses how they may inform the development of a practical thermography detection methodology.

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Section: Existing Detection Techniquesmentioning

confidence: 99%

Modelling Rail Thermal Differentials Due to Bending and Defects

et al. 2021

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“…Further propagation of RCF defects can result in rail breakages, which are still one of the main hazards of major train accidents (i.e. derailments) [4]. Therefore, developing advanced inspection technologies to inspect RCF defects more efficiently and accurately is always significant to improve the safety and performance of railway operations.…”

Section: Introductionmentioning

confidence: 99%

Method for automatic railway track surface defect classification and evaluation using a laser‐based 3D model

Stewart

Zhang

et al. 2020

IET image process

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“…Rail defect research is pivotal for railway companies [1]. Therefore, they have put plenty of effort into rail defect detection and related maintenance [2][3][4][5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Rail Weld Defect Prediction and Related Condition-Based Maintenance

Yao

Jia²,

Tao

2020

IEEE Access

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Rail weld defects are major threats to railroad transportation. Enormous resources have been required for related maintenance. This paper presents a creative solution to predict weld defects and to classify railroads into different conditions based on the predictions. The results are based on features extracted from manufacturing technologies of welds, from related materials and from influential factors in the environments. Features such as marks for welding engineers are defined. Maintenance can be selectively implemented based on the predicted conditions. Safety is the foundation of the railroad business, and a very strict safety requirement is utilized as one of the main constraints in this research. Additionally, 11 key risk factors leading to rail defects and their risk levels are identified. Extreme learning machine (ELM), random forest, logistic regression, principal component analysis (PCA), support vector machine (SVM) and other data science approaches are utilized. The evaluation results show that the related rail maintenance workload can decrease significantly under high safety standards. Labor costs of weld inspection will be reduced substantially because of the decreased workload for the sections predicted to not have any defects with a 100% recall rate (approximately 30% of the total sections), contributing to a massive cost reduction. Consequently, rail companies are expected to achieve enhanced management and operation.

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Rail Flaw Detection Technologies for Safer, Reliable Transportation: A Review

Cited by 61 publications

References 87 publications

Modelling Rail Thermal Differentials Due to Bending and Defects

Modelling Rail Thermal Differentials Due to Bending and Defects

Method for automatic railway track surface defect classification and evaluation using a laser‐based 3D model

Rail Weld Defect Prediction and Related Condition-Based Maintenance

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