Predictions of defect detection performance of air-coupled ultrasonic rail inspection system

Abstract

A rail inspection system based on ultrasonic guided waves and non-contact (air-coupled) ultrasound transduction is under development at the University of California at San Diego. The system targets defects in the rail head that are major causes of train accidents. Because of the high acoustic impedance mismatch between air and steel, the non-contact system poses severe challenges and questions on the defect detection performance. This article presents an extensive numerical study, conducted with a local interaction simulation approach, to model the ultrasound propagation and interaction with defects in the proposed system. This model was used to predict the expected detection performance of the system in the presence of various defects of different sizes and positions, and at varying levels of signal-to-noise ratios. When possible, operating variables for the model were chosen consistently with the field test of an experimental prototype that was conducted in 2014. The defect detection performance was evaluated through the computation of receiver operating characteristic curves in terms of probability of detection versus probability of false alarms. The study indicates that despite the challenges of non-contact probing of the rail, quite satisfactory inspection performance can be expected for a variety of defect types, sizes, and positions. Beyond the specific cases examined in this article, this numerical framework can also be used in the future to examine a larger variety of field test conditions.

Keywords

Rail inspection defect detection air-coupled non-contact receiver operating characteristics local interaction simulation approach ultrasound guided waves

Get full access to this article

View all access options for this article.

References

Federal Railroad Administration. Office of safety analysis, http://safetydata.fra.dot.gov/officeofsafety/default.aspx (2015, accessed 17 March 2016).

Sperry Rail Service. Rail defect manual. Danbury, CT: Sperry Rail Service, 1968.

Cannon

Edel

Grassie

et al . Rail defects: an overview. Fatigue Fract Eng M2003; 26(10): 865–886.

Lanza di Scalea

. Ultrasonic testing in railroad industry. In: Moore

(ed.) Non-destructive testing handbook. 3rd ed.Columbus, OH: American Society for Nondestructive Testing, 2007, pp. 535–552.

Anon

Rail-flaw detection. A science that works. Railway Track Struct1990; 86(5): 30–32.

Mariani

Nguyen

Phillips

et al . Non-contact air-coupled ultrasonic guided wave inspection of rails. Struct Health Monit2013; 12(5–6): 539–548.

Mariani

Nguyen

Lanza di Scalea

. Air-coupled ultrasonic testing of rails: experimental prototype and finite element simulations. Mater Eval, in press.

Mariani

Nguyen

Zhu

et al . Field test performance of noncontact ultrasonic rail inspection system. J Transp Eng: ASCE 2017; 143(5): 04017007.

Hayashi

Song

Rose

. Guided wave dispersion curves for a bar with an arbitrary cross-section, a rod and rail example. Ultrasonics2003; 41(3): 175–183.

10.

Wilcox

Evans

Pavlakovic

et al . Guided wave testing of rail. Insight: Non-Destruct Test Condition Monit2003; 45(6): 413–420.

11.

Palmer

Dixon

Edwards

et al . Transverse and longitudinal crack detection in the head of rail tracks using Rayleigh wave-like wideband guided ultrasonic waves. In: Proceedings of the nondestructive evaluation and health monitoring of aerospace materials, composites and civil infrastructure IV, San Diego, CA, 6 March 2005, SPIE 5767, pp. 70–80. Bellingham, WA: SPIE.

12.

Hesse

Cawley

. Surface wave modes in rails. J Acoust Soc Am2006; 120(2): 733–740.

13.

Kenderian

Djordjevic

Cerniglia

et al . Dynamic railroad inspection using the laser-air hybrid ultrasonic technique. Insight: Non-Destruct Test Condition Monit2006; 48(6): 336–341.

14.

Rose

Lee

Hay

et al . Rail inspection with guided waves. In: Proceedings of the 12th Asia-Pacific conference on NDT, Auckland, New Zealand, 5–10 November 2006.

15.

Long

Loveday

. Prediction of guided wave scattering by defects in rails using numerical modelling. In: Proceedings of the review of progress in QNDE, Baltimore, MD, 21–26 July 2013.

16.

Lanza di Scalea

Coccia

Bartoli

et al . Defect detection in objects using statistical approaches. Patent 8626459, USA, 2014.

17.

Lee

Staszewski

. Lamb wave propagation modelling for damage detection: I. Two-dimensional analysis. Smart Mater Struct2007; 16(2): 249–259.

18.

Lee

Staszewski

. Lamb wave propagation modelling for damage detection: II. Damage monitoring strategy. Smart Mater Struct2007; 16(2): 260–274.

19.

Paćko

Bielak

Spencer

et al . Lamb wave propagation modelling and simulation using parallel processing architecture and graphical cards. Smart Mater Struct2012; 21(7): 075001.

20.

Delsanto

Whitcombe

Chaskelis

et al . Connection machine simulation of ultrasonic wave propagation in materials. I: the one-dimensional case. Wave Motion1992; 16(1): 65–80.

21.

Delsanto

Schechter

Chaskelis

et al . Connection machine simulation of ultrasonic wave propagation in materials. II: the two-dimensional case. Wave Motion1994; 20(4): 295–314.

22.

Delsanto

Schechter

Mignogna

. Connection machine simulation of ultrasonic wave propagation in materials. III: the three-dimensional case. Wave Motion1997; 26(4): 329–339.

23.

Courant

Friedrichs

Lewy

. On the partial difference equations of mathematical physics. IBM J1967; 11(2): 215–234.

24.

Alleyne

Cawley

. A two-dimensional Fourier transform method for the measurement of propagating multimode signals. J Acoust Soc Am1991; 89(3): 1159–1168.

25.

Moser

Jacobs

. Modeling elastic wave propagation in waveguides with the finite element method. NDT & E Int1999; 32(4): 225–234.

26.

Mariani

. Non-contact ultrasonic guided wave inspection of rails: next generation approach. PhD Dissertation, University of California San Diego, La Jolla, CA, 2015, pp. 121–126, 172–185.

27.

Kay

. Fundamentals of statistical signal processing, volume II: detection theory. 1st ed. Englewood Cliffs, NJ: Prentice Hall, 1998.