Implementation and demonstration of autonomous ultrasonic track inspection using cloud-based AI rail flaw analyzer

Simple item page
dc.contributor.authorHe, Feiyang
dc.contributor.authorDurazo Cardenas, Isidro
dc.contributor.authorLi, Jian
dc.contributor.authorRuiz Carcel, Cristobal
dc.contributor.authorIshola, Ademayowa
dc.contributor.authorStarr, Andrew
dc.contributor.authorAnderson, Robert
dc.contributor.authorPrice, Richard
dc.date.accessioned2024-09-06T10:07:41Z
dc.date.available2024-09-06T10:07:41Z
dc.date.freetoread2024-09-06
dc.date.issued2024-06-07
dc.date.pubOnline2024-09-06
dc.description.abstractThis research successfully demonstrated autonomous rail inspection feasibility up to Technology Readiness Level (TRL) 7. A prototype integrating an autonomous rail vehicle and Sperry's Ultrasound Testing (UT) system was developed at Cranfield University. It was first tested at Cranfield’s Railways Innovation Test Area (RITA) at TRL 5 and tested at heritage operational railway, in Idridgehay, Derbyshire, UK achieving TRL 7. Experimental works included a 15-meter track test at RITA and nine rounds demonstration of a 250-meter track inspection at Idridgehay, showcasing inspection, localization, navigation accuracy, and defect location precision. The prototype successfully detected artificial rail defect during the demonstration and promptly communicated to command centre via email. We characterised the vehicle performance by measuring the positional error and detection rate. The positional accuracy measurements, verified through GPS and odometry, revealed an odometry-based error of 0.27-3.2 metres and an 8-metre GPS-associated error. The absence of differential GPS and a data fusion approach contributing to these errors. In addition, Weak 4G signal coverage in the fields impacted operator-vehicle communication and data uploading. Future iterations should address these limitations, exploring alternatives for enhanced accuracy and advancing defect-sizing technology.
dc.description.conferencename12th International Conference on Through-life Engineering Services – TESConf2024
dc.description.sponsorshipThis project has received funding from the Shift2Rail Joint Undertaking (JU) under grant agreement No 826255. The JU receives support from the European Union's Horizon 2020 research and innovation programme and the Shift2Rail JU members other than the Union.
dc.identifier.citationHe F, Durazo Cardenas I, Li J, et al., (2024) Implementation and demonstration of autonomous ultrasonic track inspection using cloud-based AI rail flaw analyzer. In: 12th International Conference on Through-life Engineering Services – TESConf2024, 6-7 June 2024, Cranfield, UK
dc.identifier.doi10.57996/cran.ceres-2621
dc.identifier.urihttps://doi.org/10.57996/cran.ceres-2621
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/22901
dc.language.isoen
dc.publisherCranfield University
dc.publisher.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/22901
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectAutonomous vehicle
dc.subjectrailway inspection
dc.subjectultrasonic inspection
dc.titleImplementation and demonstration of autonomous ultrasonic track inspection using cloud-based AI rail flaw analyzer
dc.typeConference paper
dcterms.coverageCranfield, UK
dcterms.dateAccepted2024-03-25
dcterms.temporal.endDate07-Jun-2024
dcterms.temporal.startDate06-Jun-2024

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Autonomous_Ultrasonic_Track_Inspection-2024.pdf
Size:
452.47 KB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.63 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

12th International Conference on Through-life Engineering Services 2024