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 Therefore, there is an obvious need to gradually replace outdated inspection methodologies with more efficient remote condition monitoring technology.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.
Evaluating the condition of a Hadfield steel crossing nose using existing inspection methods is subject to accessibility and geographical constraints. Thus, the use of conditional monitoring techniques to complement the existing inspection methods has become increasingly necessary. This paper focuses on the study of acoustic emission (AE) behaviour and its correlation with fatigue crack growth in Hadfield steel during bending fatigue tests. The probability density function for acoustic emission parameters was analysed based on the power law distribution. The results show that a sharp increase in the moving average and cumulative sum of the AE parameter can give early warning against the final failure of Hadfield steel. Two parts (Part 1 and Part 2) can be identified using the change in the slope of duration rate (dD/dN) vs. ΔK plot during the stable fatigue crack growth (FCG) process where Paris’s law is valid. The fitted power law exponent of AE parameters is smaller in Part 2 than in Part 1. The novelty of this research lies in the use of the fitted power law distribution of AE parameters for monitoring fatigue damage evolution in Hadfield steel, unlike existing AE fatigue monitoring methodology, which relies solely on the analysis of AE parameter trends.
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