01663292

Component

This paper highlights important improvements to imaging algorithms for flaw identification in railroad tracks. Utilizing advanced weighting functions in conjunction with traditional Synthetic Aperture Focus (SAF) algorithms, significant sidelobe reduction is achieved. Furthermore, by compounding various wave propagation modes in the material, artifacts are reduced. Lastly, though signal processing techniques such as baseline subtraction and deconvolution of the point spread function, the image is deblurred and defect edges are sharpened. Traditionally, due to the number of data points and pixels required, only near-real-time results are possible using the Central Processing Unit (CPU) for computation. By utilizing the parallel processing structure of the Graphical Processing Unit (GPU) architecture, significant increase in image refresh rate is achieved for real-time processing. Experimental testing of the algorithm was performed on rail sections with simulated and natural flaws acquired from the Federal Railroad Administration (FRA) Rail Defect Library

This paper was sponsored by TRB committee AR060 Standing Committee on Railway Maintenance.

18-01808

English

16p

2018

Transportation Research Board 97th Annual Meeting

Digital/other

Figures; References

Algorithms; Defects; Flaw detection; Nondestructive tests; Signal processing; Ultrasonic detectors; Wave motion

Synthetic aperture focusing technique

Maintenance and Preservation; Railroads; Vehicles and Equipment

Transportation Research Board Annual Meeting 2018 Paper #18-01808

TRIS, TRB, ATRI

Jan 8 2018 10:27AM