Quantitative monitoring of brittle fatigue crack growth in railway steel using acoustic emission. (April 2018)

• Record Type: Journal Article
• Authors: Shi, Shengrun; Han, Zhiyuan; Liu, Zipeng; Vallely, Patrick; Soua, Slim; Kaewunruen, Sakdirat; Papaelias, Mayorkinos
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 232:Number 4(2018)
• Page Start: 1211
• Page End: 1224
• Publication Date: 2018-04
• Subjects: Rail defects -- cracks -- inspection -- remote condition monitoring -- acoustic emission -- quantitative; Railroads -- Periodicals; Personal rapid transit -- Periodicals; 625.1
• DOI: 10.1177/0954409717711292
• Languages: English
• ISSNs: 0954-4097

Abstract

Structural degradation of rails will unavoidably take place with time due to cyclic bending stresses, rolling contact fatigue, impact and environmental degradation. Rail infrastructure managers employ a variety of techniques and equipment to inspect rails. Still tens of rail failures are detected every year on all major rail networks. Inspection of the rail network is normally carried out at night time, when normal traffic has ceased. As the implementation of the 24-h railway moves forward to address the increasing demand for rail transport, conventional inspection processes will become more difficult to implement. Therefore, there is an obvious need to gradually replace outdated inspection methodologies with more efficient remote condition monitoring technology. The remote condition monitoring techniques employed should be able to detect and evaluate defects without causing any reduction in the optimum rail infrastructure availability. Acoustic emission is a passive remote condition monitoring technique which can be employed for the quantitative evaluation of the structural integrity of rails. Acoustic emission sensors can be easily installed on rails in order to monitor the structural degradation rate in real time. Therefore, apart from detecting defects, acoustic emission can be realistically applied to quantify damage. In this study, the authors investigated the performance of acoustic emission in detecting and quantifying damage in rail steel samples subjected to cyclic fatigue loads during experiments carried out under laboratory conditions. Herewith, the key results obtained are presented together with a detailed discussion of the approach employed in filtering noise sources during data acquisition and subsequent signal processing.