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

B. Sambo, A. Bevan, C. Pislaru

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

The 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.

Original languageEnglish
Title of host publicationThe International Conference on Railway Engineering (ICRE) 2016
Place of PublicationBrussels, Belgium
PublisherInstitution of Engineering and Technology
Number of pages9
ISBN (Electronic)978-1-78561-293-0
ISBN (Print)978-1-78561-292-3
DOIs
Publication statusPublished - 13 May 2016
EventInternational Conference on Railway Engineering - Hotel Metropole, Brussels, Belgium
Duration: 12 May 201613 May 2016
https://communities.theiet.org/communities/events/item/201/39/10762 (Link to Conference Details)

Conference

ConferenceInternational Conference on Railway Engineering
Abbreviated titleICRE 2016
CountryBelgium
CityBrussels
Period12/05/1613/05/16
Internet address

Fingerprint

Fatigue damage
Rails
Crack propagation
Image processing
Railroad tracks
Fracture mechanics
Stress intensity factors
Sensitivity analysis
Shear stress
Wheels
Inspection
Scheduling
Hydraulics
Fatigue of materials
Planning
Defects
Fluids
Computer simulation

Cite this

Sambo, B., Bevan, A., & Pislaru, C. (2016). A novel application of image processing for the detection of rail surface RCF damage and incorporation in a crack growth model. In The International Conference on Railway Engineering (ICRE) 2016 Brussels, Belgium : Institution of Engineering and Technology. https://doi.org/10.1049/cp.2016.0521
Sambo, B. ; Bevan, A. ; Pislaru, C. / A novel application of image processing for the detection of rail surface RCF damage and incorporation in a crack growth model. The International Conference on Railway Engineering (ICRE) 2016. Brussels, Belgium : Institution of Engineering and Technology, 2016.
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abstract = "The 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.",
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Sambo, B, Bevan, A & Pislaru, C 2016, A novel application of image processing for the detection of rail surface RCF damage and incorporation in a crack growth model. in The International Conference on Railway Engineering (ICRE) 2016. Institution of Engineering and Technology, Brussels, Belgium , International Conference on Railway Engineering, Brussels, Belgium, 12/05/16. https://doi.org/10.1049/cp.2016.0521

A novel application of image processing for the detection of rail surface RCF damage and incorporation in a crack growth model. / Sambo, B.; Bevan, A.; Pislaru, C.

The International Conference on Railway Engineering (ICRE) 2016. Brussels, Belgium : Institution of Engineering and Technology, 2016.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - The 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.

KW - Feature extraction

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KW - Railway safety

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Sambo B, Bevan A, Pislaru C. A novel application of image processing for the detection of rail surface RCF damage and incorporation in a crack growth model. In The International Conference on Railway Engineering (ICRE) 2016. Brussels, Belgium : Institution of Engineering and Technology. 2016 https://doi.org/10.1049/cp.2016.0521