Rail foot flaw detection based on a laser induced ultrasonic guided wave method

doi:10.1016/j.measurement.2019.106922

Measurement

Volume 148, December 2019, 106922

https://doi.org/10.1016/j.measurement.2019.106922 Get rights and content

Highlights

•
Laser induced guided wave technology for rail foot flaw detection is verified.
•
Sensor location and excitation frequency are important parameters for detection.
•
Excitation signal frequency of 20 kHz is the deciding factor in sensor placement.
•
Sensor placed nearest ahead of the crack gives best signature of crack presence.
•
FEM successfully adapted to decide equipment specifications for flaw detection.

Abstract

With the advancements in Railway Engineering in non-destructive testing technologies, a system for the fast detection of rail flaws at earliest stage is in huge demand. Such a system could result in savings in maintenance and would have less operational impact. Most of the research work in this area has been carried out for the defects in rail head and web, while defects in rail foot, which are difficult to access are responsible for derailments and accidents. This research work is focusing on detecting rail foot flaws using non-destructive, non-contact laser induced ultrasonic guided wave-based technique. This paper presents a conceptual technique based on finite element simulations of the propagation of laser induced ultrasonic guided waves to detect cracks in the rail foot. The simulations are performed at different frequencies and varying sensor positions to identify the best suited frequency and sensor location on locomotive for reliable defect detection.

Introduction

RAIL flaw detection plays an important role in context of primary concerns of heavy haul Railway Engineering for safety and reliable tonnage rail transportation [1], [2]. Early detection of cracks can prevent derailments, serious injuries to personnel and inherent economic losses owing to railway accidents occurring due to rail breakages [3], [4], [5]. Various conventional techniques have previously been used for the purpose of rail flaw detection such as dye penetration testing, vibration measurement, alternating current field measurement and acoustic techniques. On account of the fact that these methods need some contact with the rail surface, they could not be used in mobile crack detection systems [6], [7]. Inevitably, this disadvantage of the time consuming conventional methods led to the development of laser induced ultrasonic guided wave based crack detection, which does not require any contact with the rail surface, while testing [8], [9]. Moreover, there has been research carried out for crack detection using eddy current technology for inspection speed up to 150 m/s for material with less mass density such as wires, tubes and bars [10] but not for the rail foot.

The proposed method consists of a pulsed laser injection system, air coupled sensor bank and its positioning system, digital signal processing unit, a computer and data storage system which will be mounted on a locomotive. The locomotive is believed to be an ideal location for mounting these devices as they need an external power supply unit. Certainly, there is an inherent disadvantage of the non-contact type of non-destructive testing technologies in having a low signal to noise ratio, as there are issues related to impedance mismatch and loss of signal strength in the air [11], [12]. However, this could be improved with some post signal processing methodologies to reduce noise further, which will be considered and addressed in future part of research work. Laser induced ultrasonic techniques offer an additional advantage of detecting subsurface cracks throughout the entire section of the rail, which can cover head, web and foot of the rail for crack detection subject to the excitation of the guided wave [8].

This paper first presents the concept of laser induced ultrasound guided wave-based rail foot flaw detection. This is followed by a mathematical insight into ultrasound wave propagation and excitation modes of a steel specimen. Moreover, this paper presents finite element simulations to study the behavior of laser induced ultrasonic guided waves for rail foot flaw detection. Finite element simulations will help to understand the effect of excitation frequency on wave propagation which will further help to decide the installation locations of the laser source and sensor on the locomotive. In addition, finite element simulations will be helpful to decide the equipment specification. Different excitation signal frequencies and different sensor positions are used to identify the best signal frequency and sensor position for reliable crack detection through finite element simulations.

The concept of laser ultrasonic based flaw detection is presented in Section 2. Section 3 presents literature review. The methodology is presented in Section 4. Section 5 presents fundamentals of ultrasonic wave propagation. Finite element modelling is presented in Section 6. Results are presented in Section 7. Discussion is presented in Section 8. Section 9 covers the conclusion and Section 10 presents future work.

Section snippets

Concept of laser ultrasonic based rail flaw detection

When high energy laser pulses are applied to the rail surface, the ultrasonic waves in the frequency range of 20 kHz to a few GHz propagate in the longitudinal direction along the rail. When these waves, which are of an elastic nature, meet any deformity or crack in the rail, they reflected back and scatter and carries signature of geometry and elastic properties of media.

These reflected waves, when sensed through air coupled sensors, give an indication of the presence of rail flaws [8], [9],

Literature review

Over the past few years, laser induced ultrasonics has been successfully applied in the fields of material thickness measurement, material characterization, laser cutting machines and medical technology [3], [9], [12], [17]. Commercially, use of laser ultrasonics is in the frequency range of 50 kHz to 20 MHz [8]. Laser induced ultrasonic waves could propagate to longer distances if the frequencies are limited in the kHz range, while wave travel is only limited to a few millimeters in the MHz

Methodology

For this research work, finite element software is used for studying the amplitude and frequency parameters which can produce changes in physical parameters of wave propagation such as distance travelled by the waves and its attenuation.

The proposed method of a mobile rail foot crack detection system using laser induced ultrasonic guide wave technology is shown in Fig. 1.

The methodology involves the identification of excitation frequency of laser source by varying the excitation frequency.

Fundamentals of wave propagation

For studying the ultrasonic wave generation in a steel section, a pulsed sine signal is simulated as a laser beam to generate ultrasonic guided waves in the rail foot. A pulsed sine signal of frequency f = ω/2π is simulated as representative of the laser beam applied to the rail foot. Due to the application of force, vibrations take place at the applied frequency and the equation of motion takes the form given in Eq. (1). $\frac{\partial^{2} ϕ}{\partial t^{2}} = c^{2} \frac{\partial^{2} ϕ}{\partial x^{2}} \frac{1}{2}$

Here, $c = ω / k$ represents velocity of wave where $ω$ is the

Finite element modelling

For studying the behavior of laser induced ultrasonic guided waves and their propagation in the rail section, a finite element simulation model is developed and is presented in this section. A numerical modelling method is implemented to study the behavior of ultrasonic waves in the rail sections using finite element software [29]. The finite element analysis utilizes a mathematical sub-domain (small element) approach to simulate the guided wave propagation in the rail section [30], [29], [31].

Simulation results

The results of all the simulations carried out for this research work are presented in this section.

Discussion

The results obtained from finite element simulations performed for different sensor locations conclude that the smaller the distance between the crack and sensor, the clearer is the signature captured by the sensor revealing the presence of the crack in the rail foot section. This is due to the fact that if the sensor is position is farther then before the reflection from the crack reaches the sensor the other reflections from the edge of the rail foot are captured by sensor and hence resulted

Conclusion

The technology of using laser induced guided wave propagation to detect cracks in rail foot sections has been verified through this research work with the help of finite element simulations. The concept of crack detection in the rail foot with the help of ultrasonic guided waves is presented in the paper along with mathematical modelling and finite element simulations. This study was required as the cracks in the rail foot is hard to detect and there has not been much research done on this

Future work

Further for the future work, experimental investigations will be done, and a prototype will be developed to verify the proposed concept of crack detection in rail foot with moving vehicle. Experimental parameters and data’s will be taken from this research work for the excitation frequency and positioning sensors on the locomotive. Furthermore, a virtual prototype will be developed to verify the results and an analysis on the combined uncertainty of the system will be developed. In addition, an

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

The author’s would like to gratefully acknowledge the financial support from Australasian Centre for Rail Innovation (ACRI), Grant number: HH01B – Evaluating infrared imaging and laser ultrasonics as detectors of rail foot flaws for project HH#1 titled “Moving Vehicle Rail Foot Flaw Detection”. The author’s would also like to acknowledge the support from Centre for Railway Engineering, CQUniversity and the High Performance Computing facility provided by them for this work.

References (40)

M.T. Baysari et al.
Understanding the human factors contribution to railway accidents and incidents in Australia
Accid. Anal. Prev.
(2008)
G.D. Edkins et al.
The influence of sustained attention on railway accidents
Accid. Anal. Prev.
(1997)
R. Clark
Rail flaw detection: overview and needs for future developments
NDT and E Int.
(2004)
P. Wang et al.
Features extraction of sensor array based PMFL technology for detection of rail cracks
Measurement
(2014)
B. Weekes et al.
Eddy-current induced thermography—probability of detection study of small fatigue cracks in steel, titanium and nickel-based superalloy
NDT and E Int.
(2012)
A. Cavuto et al.
Train wheel diagnostics by laser ultrasonics
Measurement
(2016)
F. Mabrouki et al.
Frictional heating model for efficient use of vibrothermography
NDT and E Int.
(2009)
Y. Choi et al.
Aircraft integrated structural health monitoring using lasers, piezoelectricity, and fiber optics
Measurement
(2018)
A. Aindow et al.
Laser-based non-destructive testing techniques for the ultrasonic characterization of subsurface flaws
NDT Int.
(1984)
R.E. Green
Non-contact ultrasonic techniques
Ultrasonics
(2004)

S. Dixon et al.

Detection of cracks in metal sheets using pulsed laser generated ultrasound and EMAT detection

Ultrasonics

(2011)

P. Wilcox et al.

The effect of dispersion on long-range inspection using ultrasonic guided waves

NDT and E Int.

(2001)

A. Cruzado et al.

Finite element modeling of fretting wear scars in the thin steel wires: application in crossed cylinder arrangements

Wear

(2014)

B. Zhang et al.

Ductile failure analysis and crack behavior of X65 buried pipes using extended finite element method

Eng. Fail. Anal.

(2014)

N.K. Mandal

Ratchetting of railhead material of insulated rail joints (IRJs) with reference to endpost thickness

Eng. Fail. Anal.

(2014)

J. Wang et al.

Numerical simulation of laser-generated ultrasound in non-metallic material by the finite element method

Opt. Laser Technol.

(2007)

I. Bartoli et al.

Modeling guided wave propagation with application to the long-range defect detection in railroad tracks

NDT and E Int.

(2005)

S. Alahakoon et al.

Rail flaw detection technologies for safer, reliable transportation: a review

J. Dyn. Syst. Meas. Contr.

(2018)

M.P. Papaelias et al.

A review on non-destructive evaluation of rails: state-of-the-art and future development

Proc. Inst. Mech. Eng., Part F: J. Rail Rapid Transit.

(2008)

C.B. Scruby et al.

Laser Ultrasonics Techniques and Applications

(1990)

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^☆: This work was supported by the Australasian Centre for Rail Innovation as part of their Project – “Moving Vehicle Rail Foot Flaw Detection.”

View full text

Rail foot flaw detection based on a laser induced ultrasonic guided wave method☆

Highlights

Abstract

Introduction

Section snippets

Concept of laser ultrasonic based rail flaw detection

Literature review

Methodology

Fundamentals of wave propagation

Finite element modelling

Simulation results

Discussion

Conclusion

Future work

Declaration of Competing Interest

Acknowledgments

Accid. Anal. Prev.

Accid. Anal. Prev.

NDT and E Int.

Measurement

NDT and E Int.

Measurement

NDT and E Int.

Measurement

NDT Int.

Ultrasonics

Ultrasonics

NDT and E Int.

Wear

Eng. Fail. Anal.

Eng. Fail. Anal.

Opt. Laser Technol.

NDT and E Int.

Rail flaw detection technologies for safer, reliable transportation: a review

J. Dyn. Syst. Meas. Contr.

A review on non-destructive evaluation of rails: state-of-the-art and future development

Proc. Inst. Mech. Eng., Part F: J. Rail Rapid Transit.

Laser Ultrasonics Techniques and Applications