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Data fusion strategies for rail flaw detection with increased reliability
conference contribution
posted on 2018-05-18, 00:00 authored by Sanath AlahakoonSanath Alahakoon, Maksym SpiryaginMaksym Spiryagin, Colin ColeColin Cole, Yan SunYan SunThere is an ever increasing demand for higher tonnages to be transported in heavy haul trains. Early detection of rail flaws ensures transportation safety and averts unplanned downtime. Several conventional
non-destructive techniques have been proposed for rail flaw detection including eddy current, infrared, vibration and laser induced ultrasonic guided wave based detection techniques. The relatively complex cross-section of a rail together with the presence of supporting sleepers and rail fastening clips also adds complexity of a different dimension to the flaw detection problem. Various flaw detection methods show differences in reliability over the cross section as well as along the longitudinal direction of the rail. This paper focuses on a survey of detection methods that can be combined together to achieve flaw detection with increased reliability. The paper also surveys the data fusion strategies that can be incorporated along with those chosen detection methods in order to obtain a complete highly reliable flaw detection strategy. The outcome of this work provides a comprehensive investigation of the potential for a high speed rail flaw detection methodology that combines more than one flaw detection method for the purpose of increasing the detection reliability.
Funding
Category 3 - Industry and Other Research Income
History
Editor
Gräbe PJ; Fröhling RDStart Page
259End Page
266Number of Pages
8Start Date
2017-09-04Finish Date
2017-09-06ISBN-13
9780911382662Location
Cape Town, South AfricaPublisher
International Heavy Haul Association (IHHA)Place of Publication
Virginia Beach, VA.Full Text URL
Peer Reviewed
- Yes
Open Access
- No
Author Research Institute
- Centre for Railway Engineering
Era Eligible
- Yes