A method for in-field railhead crack detection using digital image correlation

References

• Heiming M, Candfield J, Lochman L. Track Maintenance & Renewal; 2012. Available from: http://www.cer.be/sites/default/files/publication/2353_7473-11_MARKET_STRATEGY_A4_FINAL.pdf.
   Google Scholar
• Cannon DF, Edel KO, Grassie SL, et al. Rail defects: an overview. Fatigue Fracture Eng Mater Struct. 2003;26(10):865–886.
   Web of Science ®Google Scholar
• Lidén T, Joborn M. Dimensioning windows for railway infrastructure maintenance: cost efficiency versus traffic impact. J Rail Transp Plann Manage. 2016;6(1):32–47.
   Web of Science ®Google Scholar
• Rowshandel H, Nicholson GL, Davis CL, et al. A robotic approach for NDT of RCF cracks in rails using an ACFM sensor. Insight: Non-Destructive Testing and Condition Monitoring. 2011;53(7):368–376.
   Google Scholar
• Innotrack. D4.4.1 – rail inspection technologies. : Innotrack; 2008. www.innotrack.eu
   Google Scholar
• Marais JJ, Mistry KC. Rail integrity management by means of ultrasonic testing. Fatigue Fracture Eng Mater Struct. 2003;26(10):931–938.
   Web of Science ®Google Scholar
• Rajamäki J, Vippola M, Nurmikolu A, et al. Limitations of eddy current inspection in railway rail evaluation. Proc Inst Mech Eng F J Rail Rapid Transit. 2018;232(1):121–129.
   Web of Science ®Google Scholar
• Kishore M, Park J, Song S, et al. Characterization of defects on rail surface using eddy current technique. J Mech Sci Technol. 2019;33(9):4209–4215.
   Web of Science ®Google Scholar
• Pohl R, Erhard A, Montag HJ, et al. Ndt techniques for railroad wheel and gauge corner inspection. NDT E Int. 2004;37(2):89–94.
   Web of Science ®Google Scholar
• Gao Y, Tian GY, Li K, et al. Multiple cracks detection and visualization using magnetic flux leakage and eddy current pulsed thermography. Sens Actuators A. 2015;234:269–281.
   Google Scholar
• Greene RJ. Crack detection in rail using infrared methods. Opt Eng. 2007;46(5):051013.
   Web of Science ®Google Scholar
• Abidin IZ, Tian GY, Wilson J, et al. Quantitative evaluation of angular defects by pulsed eddy current thermography. NDT E Int. 2010;43(7):537–546.
   Web of Science ®Google Scholar
• Zhan Y, Dai X, Yang E, et al. Convolutional neural network for detecting railway fastener defects using a developed 3D laser system. Int J Rail Trans. 2021;9(5):424–444. Available from.
   Web of Science ®Google Scholar
• Safa M, Sabet A, Ghahremani K, et al. Rail corrosion forensics using 3D imaging and finite element analysis. Int J Rail Trans. 2015;3(3):164–178.
   Web of Science ®Google Scholar
• Deutschl E, Gasser C, Niel A, et al. Defect detection on rail surfaces by a vision based system. In: IEEE Intelligent Vehicles Symposium, 2004, Parma, Italy; IEEE; 2004. p. 507–511.
   Google Scholar
• Vijaykumar VR, Sangamithirai S. Rail defect detection using Gabor filters with texture analysis. 2015 3rd International Conference on Signal Processing, Communication and Networking, ICSCN 2015, Chennai, India. 2015:6–11.
   Google Scholar
• Zhuang L, Wang L, Zhang Z, et al. Automated vision inspection of rail surface cracks: a double-layer data-driven framework. Transp Res Part C Emerging Technol. 2018;92(May):258–277.
   Google Scholar
• Lee JS, Hwang SH, Choi IY, et al. Estimation of crack width based on shape-sensitive kernels and semantic segmentation. Struct Control Health Monit. 2020;27(4):1–21.
   Web of Science ®Google Scholar
• Mohan A, Poobal S. Crack detection using image processing: a critical review and analysis. Alexandria Eng J. 2018;57(2):787–798.
   Web of Science ®Google Scholar
• Jessop C, Ahlström J, Persson C, et al. Damage evolution around white etching layer during uniaxial loading. Fatigue Fracture Eng Mater Struct. 2020;43(1):201–208.
   Web of Science ®Google Scholar
• Meyer KA, Nikas D, Ahlström J. Microstructure and mechanical properties of the running band in a pearlitic rail steel: comparison between biaxially deformed steel and field samples. Wear. 2018;396-397:12–21.
   Web of Science ®Google Scholar
• Turner D. Digital image correlation engine (dice). Sandia Report, SAND2015-10606 O; 2015.
   Google Scholar
• Meyer KA, Gren D. Rail crack microscopy sections. Available from. 2021; DOI:10.17632/h56kbg2h52.1
   Google Scholar
• Meyer KA. A simple model to calculate rail bending. Available from. 2021; DOI:10.17632/h355wmc5cf.1
   Google Scholar
• Nielsen JCO, Kabo E, Ekberg A. Alarm limits for wheel–rail impact loads – part 1: rail bending moments generated by wheel flats. Gothenburg: Chalmers University of Technology; 2009. Available from: https://research.chalmers.se/en/publication/107339.
   Google Scholar
• Li X, Nielsen JC, Torstensson PT. Simulation of wheel–rail impact load and sleeper–ballast contact pressure in railway crossings using a Green’s function approach. J Sound Vib. 2019;463:1–16.
   Web of Science ®Google Scholar
• Robnett QL, Thompson MR, Hay WW, et al. Technical data bases report, Ballast and foundation materials research program, Report no. FRA-OR&D-76-138. Washington, D.C: U.S. Department of Transportation; 1975. Available from: https://railroads.dot. gov/sites/fra.dot.gov/files/fra_net/15881/1975_TECHNICAL%20DATA% 20BASES%20REPORT%20BALLAST%20AND%20FOUNDATION.PDF.
   Google Scholar
• Meyer KA, Ekh M, Ahlström J. Modeling of kinematic hardening at large biaxial deformations in pearlitic rail steel. Int J Solids Struct. 2018;130-131:122–132.
   Web of Science ®Google Scholar
• Stock R, Pippan R. Rail grade dependent damage behaviour - Characteristics and damage formation hypothesis. Wear. 2014;314(1–2):44–50.
   Web of Science ®Google Scholar
• ASTM International. A388/A388M Standard Practice for Ultrasonic Examination of Steel Forgings; 2019.
   Google Scholar
• Janatabadi F, Mohammadzadeh S, Nouri M. A robust complementary index for railway maintenance planning based on a probabilistic approach. Int J Rail Trans. 2021;9(4):380–404. Available from.
   Web of Science ®Google Scholar
• Li Z, Zhao X, Dollevoet R. An approach to determine a critical size for rolling contact fatigue initiating from rail surface defects. Int J Rail Trans. 2017;5(1):16–37.
   Web of Science ®Google Scholar
• Kouroussis G, Kinet D, Moeyaert V, et al. Railway structure monitoring solutions using fibre Bragg grating sensors. Int J Rail Trans. 2016;4(3):135–150.
   Web of Science ®Google Scholar
• Phantom t1340 camera [https://www.phantomhighspeed.com/products/cameras/tseries/t1340]; Accessed: 2021August20.
   Google Scholar
• Etoh TG, Nguyen AQ, Kamakura Y, et al. The theoretical highest frame rate of silicon image sensors. Sensors. 2017;17(3):8–13.
   Web of Science ®Google Scholar