A novel application of image processing for the detection of rail surface RCF damage and incorporation in a crack growth model

Abstract

A novel application of image processing for the detection of rail surface RCF damage and incorporation in a crack growth model Original CitationSambo, Bello, Bevan, Adam and Pislaru, Crinela (2016) A novel application of image processing for the detection of rail surface RCF damage and incorporation in a crack growth model. In: International Conference on Railway Engineering 2016 (ICRE), 12th 13th May 2016, Brussels, Belgium.This version is available at http://eprints.hud.ac.uk/id/eprint/28497/ The University Repository is a digital collection of the research output of the University, available on Open Access. Copyright and Moral Rights for the items on this site are retained by the individual author and/or other copyright owners. Users may access full items free of charge; copies of full text items generally can be reproduced, displayed or performed and given to third parties in any format or medium for personal research or study, educational or not for profit purposes without prior permission or charge, provided:• The authors, title and full bibliographic details is credited in any copy;• A hyperlink and/or URL is included for the original metadata page; and • The content is not changed in any way. Keywords: surface crack detection; feature extraction; image processing segmentation; railway safety; rolling contact fatigue crack growth simulation. AbstractThe paper presents the development of an intelligent image processing algorithm capable of detecting fatigue defects from images of the rail surface. The links between the defect detection algorithm and 3D models for rail crack propagation are investigated, considering the influence of input parameters (materials, vehicle characteristics, loading conditions). The dynamic behaviour at the wheel-rail interface resulting in contact forces responsible for stressing and straining the rail material are imported from vehicle dynamics simulations. The integration of the simulated results from vehicle dynamics, contact and fracture mechanics models offer more reliable estimation of the stress intensity factors (SIF). Also the sensitivity analysis related to materials, vehicle characteristics, and loading conditions will provide further understanding of the factors that influence crack propagation in rails such as shear stresses, hydraulic pressure, fluid entrapment and squeeze film effect. This novel application of image processing for the detection of rail surface rolling contact fatigue (RCF) damage and automatic incorporation in a crack growth model represents an important contribution to the development of modern techniques for non-destructive rail inspection. This will result in improved planning/scheduling of future rail maintenance (e.g. rail grinding, renewal), less disruptions and reduced track maintenance costs in rail industry.

Crack Detection using Image Processing for Automobiles and Aircrafts

“…In these structures, one of the courses in judging the auxiliary wellbeing is to look at a break on the surface of the structure. Since the state of a solid structure can be effortlessly and straightforwardly distinguished by reviewing the surface break, the crack appraisal ought to be done all the time to guarantee sturdiness and wellbeing inside its life-cycle [14]. So many viable conclusions were drawn from this paper which helped us to determine better and concise results with a better accuracy and Precision.…”