A laser scanner based approach for identifying rail surface squat defects. (July 2021)

• Record Type: Journal Article
• Authors: De Becker, D; Dobrzanski, J; Justham, L; Goh, YM
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 235:Number 6(2021)
• Page Start: 763
• Page End: 773
• Publication Date: 2021-07
• Subjects: Robotic inspection -- railway technology -- RCF -- research and development -- simulation -- squats -- visual inspection wear; Railroads -- Periodicals; Personal rapid transit -- Periodicals; 625.1
• DOI: 10.1177/0954409720962252
• Languages: English
• ISSNs: 0954-4097

Abstract

The defect identification process within the UK rail industry has seen significant improvements over the past decade with the introduction of new measurement systems and defect detection systems. Although significant work has been on the defect identification little work has been done on the process after the defect has been detected. This repair process is still extremely manual. Due to the current process being manual the repair operation has very little traceability and transparency. This paper has therefore presented the need for not only a defect detection system but a defect repair system for the UK railway industry. Further to this, this paper has acknowledged that the rise of defects occurring on the UK railway lines requires a solution that can fully repair a defect with little to no user intervention in a timely manner. To address this, this paper has taken the extremely manual process of rail repair and has laid out the possibilities to automate this process. By doing this a work flow diagram has been generated to show how the system could be used to repair surface defects with a specific focus being made on squat defects. To achieve this a defect detection and measurement system has been explored, as this will make up the first stage of the automated repair system. The literature on various defect detection algorithms was reviewed and two variations of existing defect detection algorithms were created, i.e. the Covariance method and the Normal Intersection method. These algorithms have been tested against 100 simulated squat defects and have been verified using 4 experimentally generated defects. Both algorithms have been proven to not only identify the approximate size of the defect but also its location. This successful defect identification will be integrated into an automated rail repair system.