Nondestructive Testing Technologies for Rail Inspection: A Review

Alongside the development of high-speed rail, rail flaw detection is of great importance to ensure railway safety, especially for improving the speed and load of the train. Several conventional inspection methods such as visual, acoustic, and electromagnetic inspection have been introduced in the pa...

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Bibliographic Details
Published in:Coatings (Basel) 2022-11, Vol.12 (11), p.1790
Main Authors: Gong, Wendong, Akbar, Muhammad Firdaus, Jawad, Ghassan Nihad, Mohamed, Mohamed Fauzi Packeer, Wahab, Mohd Nadhir Ab
Format: Article
Language:English
Subjects:
Accuracy
Artificial intelligence
Automation
Cameras
Corrosion
Data analysis
Defects
Electromagnetism
Flaw detection
High speed rail
High speed trains
Innovations
Inspection
Machine learning
Materials
Methods
Nondestructive testing
Passenger rail services
Sensors
Signal processing
Stress concentration
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Summary:Alongside the development of high-speed rail, rail flaw detection is of great importance to ensure railway safety, especially for improving the speed and load of the train. Several conventional inspection methods such as visual, acoustic, and electromagnetic inspection have been introduced in the past. However, these methods have several challenges in terms of detection speed and accuracy. Combined inspection methods have emerged as a promising approach to overcome these limitations. Nondestructive testing (NDT) techniques in conjunction with artificial intelligence approaches have tremendous potential and viability because it is highly possible to improve the detection accuracy which has been proven in various conventional nondestructive testing techniques. With the development of information technology, communication technology, and sensor technology, rail health monitoring systems have been evolving, and have become equally significant and challenging because they can achieve real-time detection and give a risk warning forecast. This paper provides an in-depth review of traditional nondestructive techniques for rail inspection as well as the development of using machine learning approaches, combined nondestructive techniques, and rail health monitoring systems.
ISSN:2079-6412
2079-6412
DOI:10.3390/coatings12111790