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Review Article

Rail Flaw Detection Technologies for Safer, Reliable Transportation: A Review OPEN ACCESS

[+] Author and Article Information
Sanath Alahakoon

Centre for Railway Engineering,
Central Queensland University,
Gladstone, Queensland 4680, Australia;
Australasian Centre for Rail Innovation,
Canberra ACT 2608, Australia
e-mail: s.alahakoon@cqu.edu.au

Yan Quan Sun

Centre for Railway Engineering,
Central Queensland University,
Rockhampton, Queensland 4702, Australia;
Australasian Centre for Rail Innovation,
Canberra ACT 2608, Australia
e-mail: y.q.sun@cqu.edu.au

Maksym Spiryagin

Centre for Railway Engineering,
Central Queensland University,
Rockhampton, Queensland 4702, Australia;
Australasian Centre for Rail Innovation,
Canberra ACT 2608, Australia
e-mail: m.spiryagin@cqu.edu.au

Colin Cole

Centre for Railway Engineering,
Central Queensland University,
Rockhampton, Queensland 4702, Australia;
Australasian Centre for Rail Innovation,
Canberra ACT 2608, Australia
e-mail: c.cole@cqu.edu.au

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received February 20, 2017; final manuscript received July 3, 2017; published online September 25, 2017. Assoc. Editor: Shankar Coimbatore Subramanian.

J. Dyn. Sys., Meas., Control 140(2), 020801 (Sep 25, 2017) (17 pages) Paper No: DS-17-1110; doi: 10.1115/1.4037295 History: Received February 20, 2017; Revised July 03, 2017

This paper delivers an in-depth review of the state-of-the-art technologies relevant to rail flaw detection giving emphasis to their use in detection of rail flaw defects at practical inspection vehicle speeds. The review not only looks at the research being carried out but also investigates the commercial products available for rail flaw detection. It continues further to identify the methods suitable to be adopted in a moving vehicle rail flaw detection system. Even though rail flaw detection has been a well-researched area for decades, an in-depth review summarizing all available technologies together with an assessment of their capabilities has not been published in the recent past according to the knowledge of the authors. As such, it is believed that this review paper will be a good source of information for future researchers in this area.

Rails are routinely subjected to high mechanical loads and harsh environmental conditions. The main loading components are rolling contact pressure, shear, and bending forces from the vehicle weight, thermal stresses due to restrained elongation of continuously welded rails, and residual stresses from manufacturing (roller straightening) and welding in the field [1]. These stress distributions are illustrated in Fig. 1 [2].

At heavy axle loads, the bending and thermal stresses in the rail are sufficiently high that any small notch or other damage in the foot of the rail will result in fatigue crack initiation and growth. Despite significant technological advancements made toward the safety of trains and associated railway track maintenance strategies, rail flaws resulting in breakages of rails still occur frequently around the world leading to major train derailments. Although there has been considerable research into various aspects of rail's “permanent way,” there has not been a lot centered on rail foot flaw detection despite its obvious contribution to rail breakage.

Hence, new technologies for detecting rail flaws are urgently required in order to improve the safety and performance of railway operations through reducing the risks of broken rails and potentially disastrous consequences (i.e., derailments). However, with the need for monitoring thousands of kilometers of rail track on a regular basis, the biggest challenge for researchers is to develop flaw detection methodologies suitable for moving vehicle rail flaw detection at higher speeds.

At this point, it is also important to present a brief summary of typical location, dimension, and orientation of those rail flaws. Such a summary is presented in Table 1 [3]. In addition to different types of rail defects summarized in Table 1, it must also be noted here that there can be following rail defects causing rail breakages leading to catastrophic accidents [3]:

  1. (1)Rail head defects: Local battering, flaking, long groove, and line (manufacturing defect) are the major head defects.
  2. (2)Rail web defects: Horizontal crack at the web-head filet radius, horizontal crack at the web foot filet radius, vertical (longitudinal) splitting of the web, bolt hole fatigue within fishplate limits, diagonal cracking at holes outside fishplate limits, diagonal cracking not passing through a hole, lap (surface defect generally appears as a line parallel to the axis of rolling on the web surfaces), horizontal crack at web-head filet radius, and excessive web corrosion are the major web defects.
  3. (3)Foot defects: Vertical (longitudinal) crack in foot, transverse crack, or fracture starting from the rail seat and transverse crack or fracture starting from the rail foot away from the rail seat are the major foot defects.

This summary gives a good understanding on the challenges any rail flaw detection methodology will have in ensuring the detection reliability.

The aim of this review paper is to thoroughly research possible instrumentation technologies that are already available, which could be used to address the particular measurement and detection needs of moving vehicle rail flaw detection systems. In Secs. 2 and 3 of the paper, a thorough literature review of already reported rail flaw detection methodologies giving emphasis to their suitability for moving vehicle rail flaw detection systems will be presented together with some classifications of the available methodologies in terms of technology used, direction of travel of the detection signal, etc.

In this section, already reported work on rail flaw detection will be reviewed. The methods described are not in any particular order.

Infrared Imaging Approach for Rail Crack Detection.

This crack detection method for rail utilizes the change in infrared (IR) emission of the rail surface during the passage of a train wheel. The underlying principle of infrared thermography-based crack detection in rails is the fact that infrared radiation emitted from a surface changes during a change in stress condition. When a wheel passes over a section of track, a bending stress is induced in the rail, and this is relaxed once the wheel has moved away. Since the rail is kept within the limits of elastic behavior during this loading event, the rail will undergo a minute transient temperature change when loaded: the rail head will be in relative compression and will experience a slight temperature rise, and the rail foot will be in relative tension and will experience a slight temperature drop. The greater concern is the possible presence of cracks or other microscale defects in the rail, which may lead to crack propagation and subsequent failure. The temperature variations explained earlier lead to changes in infrared emission from the rail surface as the rail wheel passes by. This makes it possible to introduce the term “infrared response” of the rail. What is normally done is comparing the actual loaded infrared response with the expected or nominal loaded infrared response. This makes it a differential measurement. The difference between the two infrared images will generate a null data field except where a defect is present. If the defect is a surface-connected crack, the resulting crack tip stress distribution can then be quantified to assess the degree of the crack. This leads to estimates of the remaining fatigue life depending on the location of the crack in the rail section. This method is well described in Ref. [4].

Sonic Infrared Imaging.

Another measurement method closely related to the previously mentioned method is sonic infrared (SIR) imaging [5], where a short pulse of low frequency ultrasound is injected to cause the crack surfaces to rub or clap. This rubbing or clapping induces frictional heating within the rail in the vicinity of the crack. The heating is then observed by the use of an IR video camera. Figure 2 shows the schematic diagram of the detection system for fatigue cracks. This method uses an ultrasonic transducer placed in some convenient location on the rail having a suitable contact surface. The infrared camera is placed anywhere with visible access to the inspected area. In another approach in Ref. [6], authors have presented the nonlinear behavior of the coupling material due to impedance mismatch to the vibrations, which resulted in variations in temperature at the cracks when subjected to infrared imaging. The authors also indicated that choosing the right coupling material is very important. The coupling material for the target should be such that it has a higher transmission coefficient than for the sample, as this will result in higher thermal energy and hence higher detectability of the cracks using sonic IR imaging technology. Finite element analysis (FEA) and its experimental validations have also been performed to support the work. An experimental validation of FEA calculations is presented in Ref. [7] for detecting surface and subsurface cracks in metals using SIR imaging. The authors concluded that the heating of the metals while carrying out crack detection using SIR is mainly due to friction. A short pulse of low frequency is used for heating the cracked surface in this technique. These investigations were only performed for rectangular cracks. Also, it has been concluded that the sound excitation at higher power levels (chaotic excitation) produces more heating due to the higher differential motion at the crack as compared to nonchaotic excitation. Studying and performing an experiment to determine the effect of crack closure due to clamping in sonic infrared imaging is the contribution in Ref. [8]. The authors have concluded that there is less heat generation at cracks due to the effects of clamping (at clamping forces of 300–400 N) resulting from fewer vibrations, and hence it is difficult to capture useful images using an infrared video camera. A sonic excitation source with mechanical energy is used to cause friction at the crack surface for a short pulse. The authors have analyzed a technique to study the effect of transducer engagement force on crack detection in Ref. [9]. A 20 kHz Branson ultrasound source is used as the source of excitation. The resulting images captured by a thermal camera proved that, as the engagement force of the transducer increases, the intensity of the glow from crack images decreases. This happens due to less vibration, less thermal energy, and hence less heat generation because of reduced relative velocity between the surfaces, as stated in this paper.

Pulse Eddy Current Infrared Imaging.

Another approach uses infrared imaging as the final sensing method while using pulsed eddy currents (PEC) to generate heating around cracks. This method is called pulse eddy current imaging [10]. In PEC thermography (also known as induction thermography), a short burst of electromagnetic excitation is applied to the material under inspection, inducing eddy currents to flow in the material. When these eddy currents encounter a discontinuity while traveling inside the rail, they are forced to divert from their normal path. This leads to the creation of areas of increased and decreased eddy current density. Areas with increased eddy current density experience higher levels of Joule (Ohmic) heating. As such, the defect can be identified from the IR image sequence, both during the heating period and during cooling. This method is elaborated in Ref. [10]. The same technique is used in Refs. [1113]. Figure 3 shows the top view of a pulse eddy current probe aligned in the direction of magnetic induction flux [12].

Peng et al. [14] have developed a nondestructive testing (NDT) method to detect angled defects in rails using a Helmholtz coil for heating. The paper presents simulations based on FEA and experimental results. The experimental validation concludes that the uniform magnetic field excitation covers a larger area and gives a more stable temperature distribution as compared to nonuniform magnetic field excitation. In addition, it is verified by the simulations and experiments performed that pulsed eddy current thermography along with uniform magnetic field excitation can be used effectively to characterize the angled defects. A PEC NDT technique for crack detection is presented in Ref. [15]. A pancake exciting coil for creating a magnetic field and a pick up coil for detecting the disturbed magnetic field due to cracks is used for this purpose. Also, a new PEC probe is proposed, which uses a self-differential technique and has higher accuracy in quantifying defects. It requires no reference signal to be subtracted from the received signal so as to analyze the defect from the difference signal. A 10 V pulse is used to excite with a repetition rate of 100 Hz (from a direct digital synthesizer chip AD7008) with pulse width of 5 ms followed by a power amplifier to enhance the magnetic field. It was confirmed through the experiment that the peak of the scanned waveform from the traditional PEC pancake probe is directly related to the depth of the defect (due to the restriction in the structure, the excitation magnetic field is affected by the induced magnetic field due to the eddy current, which causes difficulty in capturing defect signals). The result from the new PEC probe shows that minimum peak value decreases with increase in defect depth. In Ref. [16], the authors have presented the work carried out for investigating the factors (induced current strength and crack orientation) influencing detection of surface cracks using pulsed eddy current thermography. The experiments performed concluded that a minimum heating threshold is essential for proper detection of defects. Also, it is shown that this technique is better in terms of being a noncontact type and faster as compared to other nondestructive evaluation techniques for crack detection. Regarding crack orientation, it is concluded that it is more significant in nonferromagnetic material. With this method, the cracks under thermal barrier coatings can also be detected. The authors have cited the eddy current evaluation done on various defects and orientations in Ref. [17]. Moreover, this technique has been analyzed to monitor crack growth with postsignal processing. Reliability of this technique is proven by an experiment conducted and identified as a better crack detection technique. It also states that the pulsed eddy current method offers a broad range of inspections including crack detection, material and coating thickness, conductivity measurement of material, heat damage detection, crack depth determination, and heat treatment monitoring. In addition, it offers the advantage of being contactless.

Acoustic Emission Detection Technique.

The application of acoustic emission (AE) techniques for detecting and monitoring crack growth in rail steels has been presented in Ref. [18]. The acoustic sensors in this method have been mounted on the rail web. As such, it must be mentioned here that the suitability of the method for a mobile application is doubtful. A similar approach can also be found in Ref. [19]. In this method, a sample of rail steel is cut from a rail mimicking a defect and that sample is used to observe the acoustic noise characteristics when it is excited with a dynamic load creating noise. The schematic of the rig is shown in Fig. 4 [19]. A slightly different approach where the acoustic sensor is mounted on the moving body has been presented in Ref. [20]. Acoustic methods are also popular for detection of rail wheel defects [21].

In Ref. [22], the authors have analyzed the changes in the AE parameters for different materials subjected to cyclic loads. This work also categorizes the fatigue developed in the material under load as “instant or fragile” (associated with gradual increase in internal stress), “gradual or plastic” (related to the permanent or plastic deformation of the material), and “instant gradual or fragile-ductile (consist of multistage development of fatigue failure).” The authors continue further to present the feasibility of criterion for pre-assessment of fractures for various materials. Moreover, the authors propose that the parameters of AE energy can help with the pre-assessment of microcracks. The authors report the characteristics of fatigue crack development in steel and welds in Ref. [23]. In the experimental analysis, cyclic loading at a frequency of 7.5 Hz and load of 16 kN and 10 kN were applied to the test specimen. Two broadband piezoelectric transducers were used for AE signal detection with a frequency range of 10 kHz to 2 MHz. The parameters of the AE signals are measured for base metal crack initiation, propagation, fracture, and for the weld structure. It is found that the waveform of base metal at the fracture stage is a narrow pulse with higher energy having a frequency range between 300 kHz and 400 kHz. On the contrary, in the case of welded specimen, higher amplitudes were measured in a frequency range of 300–600 kHz. In Ref. [24], the authors have proposed a method for rail health monitoring using an acoustic emission technique and its characterization. The plotted stress versus strain curve gives a clear indication of the safe or unsafe status of the rail. An AE hits criterion [24] is also established to characterize the safe and unsafe status of rail. Further, the variations of AE hits are used to assess the difference between extremely unsafe and slightly unsafe regions. The experimental analysis performed in the work proves that this method is very effective in checking the status of the rail. The authors have presented a method to detect the health of the rail at high-speed in Ref. [25]. The method used is based upon negative matrix factorization and relevance vector machine by acoustic emission signals. Stress–strain curves are used to detect the safe and unsafe status of the rails. To distinguish safe and unsafe status, frequency spectrum analysis of the AE signals is used. With the negative matrix factorization technique, the vectors obtained are optimized and compressed, which are then used as samples for relevance vector machine to train and test the classifier for accuracy. This technique proved to achieve an accuracy of up to 96%. The AE signals are obtained with the help of a tensile testing machine.

Ultrasonic Detection Techniques.

Detection mechanisms using ultrasonic techniques can vary considerably. The method and design of the systems depend on the required direction of ultrasonic wave emission into the material. Under this section, only the reported work where the ultrasound injection is normal to the surface of the rail is considered. All ultrasound detection applications where the detection mechanism depends on the travel of the ultrasound waves along the longitudinal direction of the rail will be summarized under guided wave principle-based detection techniques in Sec. 2.8.

In one approach, a piezoelectric transducer is moved over the surface of the rail, which allows ultrasound injection into the rail. The ultrasound waves traveling in the rail are reflected when they encounter flaws. These echo signals, captured by means of ultrasound techniques, are used for detecting the flaws [26]. With this method, flaws in the rail head and web can be reliably detected and identified. However, in this method, echo signals from flaws at the edges of the flanges of the rail base, which can be extremely hazardous, are detected less efficiently. This makes the problem of determining their location, shape, and size a very complex one. This problem has been solved by using coherent methods for obtaining flaw images from measured echo signals. The publication does not reveal information about any commercial product using the methodology and the results produced seem to be only by using a laboratory test system.

Ongoing research on using a linear phased array transducer clamped to a paintbrush transducer is presented in Refs. [27] and [28]. The phased array transducer has been used to physically scan across the various samples. A similar approach is reported in Ref. [29]; however, there is no clear mention about its ability to detect flaws specifically in the rail foot.

Excitation of surface wave modes is widely used with ultrasonic techniques as discussed in Refs. [3033]. However, these methods can only detect defects closer to the top surface of the rail head. Of these reported works, Hesse et al. [30,32] and Edwards et al. [33] use an improved local immersion probe that is said to perform better compared to wheel probes. This method uses a local immersion probe having an eight element transducer array placed at an angle of 300 in a casing filled with water to excite the surface waves on the rail head and also to receive the reflected surface waves due to surface defects [33]. The schematic diagram of this probe is shown in Fig. 5.

An improved ultrasonic method, which can be used to detect flaws in the rail head, and the web are presented in Ref. [34]. The method is suitable only for detecting internal defects of the rail head, web, and the area of the rail foot directly under the web.

Alternating Current-Based Detection Techniques.

Alternating current field measurement (ACFM) sensor technology is the underlying principle in this approach. The method is suitable for detecting cracking due to rolling contact fatigue (RCF) on the top surface of the rail head. ACFM sensors are said to be capable of detecting flaws at higher inspection speeds [35,36]. The ACFM technique is based on the principle that an alternating current (AC) can be induced to flow in a thin skin near the surface of any conductor. If there are no defects present in a particular area of the component under test, any induced remote uniform electrical current will not be disturbed. However, if a crack is present, the uniform electrical current will be disturbed and the current flows around the ends and down the faces of the crack. Because the current is an alternating current, it flows in a thin skin close to the surface and is unaffected by the overall geometry of the component. Associated with the current flowing in the surface is a magnetic field above the surface. This magnetic field, just like the current in the surface, will be disturbed in the presence of a defect. An important factor of the ACFM technique is its ability to relate measurements of the magnetic field disturbance to the size of defect that caused that disturbance. This is a technology capable of detecting only rail head surface defects. The way the flux lines are disturbed due to a surface defect and the definition of the field directions and coordinate system used in the method are shown in Fig. 6.

In Ref. [37], the authors present a low- and high-speed crack detection technique using an ACFM array and ACFM pencil probe, respectively. The tests conducted using the ACFM array at a maximum speed of 4 mph give accurate results with lift off variations of 1–6.5 mm. Using the ACFM pencil probe, the tests were conducted up to a speed of 20 mph with accurate results again with a lift off variation of 1–6.5 mm. However, the crack depth measurements were not so promising in the case of actual cracks, but this has been attributed to the large mouth opening of the particular cracks. The development of an automated robotic system for detection and characterization of cracks in rail tracks using an ACFM technique is presented in Ref. [38]. It states that, up to lift off 4 mm, the results obtained were promising to detect and characterize the cracks. It concludes that, in order to characterize the surface cracks accurately, the probe should be kept at a minimum probable lift off (possibly zero). The detection of crack depth is carried out using the percentage variation in the signal (when current is normal to the crack). To detect angled defects, the crack angle should be known so that the probe can be adjusted normal to the crack. Probe movement is possible with the help of a robotic arm. The work in Ref. [39] presents a noncontact type of technique for detection of cracks in the rails using an alternating current method. An alternating current with a frequency of 5 kHz is used for testing purposes. Defect depth characterization is also done with the help of software. Moreover, the effect of detecting probe position (perpendicular or slanted at an angle) is also presented in this paper. This technique is found to be effective and tolerant when the defects are away from the probe center, up to an angle of 20 deg or when they are not aligned with the direction of travel of the probe. However, defect detection is also possible at 90 deg. The defect lengths, plus minimum and maximum peaks are also possible to detect with this method. This paper performed experiments with a maximum lift off of 4 mm with the available equipment. However, other works have reported up to 10 mm lift offs. A flaw detection technique using ACFM with a constant lift off of the probe at low-speed is presented in Ref. [40]. The tests were carried out at a low-speed of 0.36–0.54 km/h. The tests were conducted with and without lift off, and it was observed that the results were more reliable without lift off in comparison to any lift off between the probe and the surface of the rail. Also, it was observed that ACFM signals are more prominent with larger depths of the cracks. This technique at low-speed also detects the shape, size, and location of the defects. The work performed confirmed the capability of this technique to detect cracks as small as 1.2 mm. In Ref. [41], the authors have presented an experimental and finite element analysis to detect RCF cracks in rails and wheels. A 5 kHz ACFM micropencil probe and two magnetic sensors are used, controlled by a robotic arm. Lift off is maintained constant throughout the detection. A combined experimental and modeling approach is presented in the paper using ACFM software, which can also measure the crack pocket length. It was observed that RCF cracks appearing in clusters affect the ACFM signals. The detection capability also depends on the spacing between the cracks. For isolated defects, it is possible to determine the size by adapting appropriate sizing techniques and analyzing spacing between the cracks visually.

Vibration-Based Detection Techniques.

The vibration-based detection system proposed in Refs. [42] and [43] utilizes a permanently laid wireless sensor network, which senses the vibrations of the rails. The sensed vibration signals are then transmitted to a central processing unit. The work has been limited to finite element simulations. However, the analysis seems to be capable of detecting flaws inside the rail foot. The practical implementation of the method has not started and it is difficult to predict if this approach could lead to a successful flaw solution in practice.

Adaptive noise cancellation (ANC) together with time frequency analysis, which in most cases is short-time Fourier transform as presented in Ref. [44], is for detecting defects on the top surface of the rail head. The installation of the accelerometers and the signal flow to the ANC filter in the detection system is shown in Fig. 7.

An approach to detect various types of defects in the rail using wavelet transformation is presented in Ref. [45]. Wavelet packet decomposition and wavelet coefficients are used for detecting defects in the crack. The signal obtained was decomposed into 12 levels and then their energy coefficients were gathered, which were then used for defect detection. The coefficients obtained from the defective rail are compared with the coefficients of healthy rail. The difference between the two provides indications regarding the presence of defects in the rail. A time frequency-based approach to detect defects present in the rail using wavelet transformation is presented in Ref. [46]. This approach is only applicable to longitudinal and lateral defects present in the rail head. This transformation successfully identified the group velocity dispersion curves and the frequency-dependent behavior of rail in the range of 1–7 kHz. The experimental results were then compared with the numerical results and found comparable. The attenuation of the vibration (both longitudinal and lateral) found to be low was induced by material damping and loose supports. The frequencies with low attenuation are identified and are then used for long range detection.

Electromagnetic Acoustic-Based Detection Techniques.

Electromagnetic acoustic transducers (EMATs) are a noncontact ultrasonic transmitting and receiving device. EMATs require no coupling and can work at standoffs of several millimeters above the sample. Nowadays, the electromagnetic acoustic technique has increasingly become a mainstream NDT method. EMATs have been successfully applied to many industrial applications. By proper adjustment of the angle of injection of the ultrasonic signals, EMATs are capable of detecting defects in the rail head and the web. However, covering the whole rail foot is not possible using this technology [47,48]. The EMAT design details are shown in Fig. 8, in which the titanium alloy wear face and the stainless steel deployment spring are clearly indicated.

A slightly different electromagnetic technology is presented in Ref. [49], where a linearly integrated hall sensor array is used to sense the magnetic field inside the rail due to a sheet type induced current. The induced magnetic field can be distorted by the existence of objects in space or cracks in the specimen. Therefore, the distribution of the magnetic field intensity can change due to the presence of the cracks. These changes are sensed by the sensor array. Once again, the geometry of the rail cross section makes it impossible for the method to be successfully adopted to detect flaws in the rail foot.

Finkel and Godinez [50] have cited the use of electromagnetic modulation of ultrasonic signals reflected from the defects to detect the flaws in NDT. An experiment has been performed with the help of a Gaussian-shaped current pulse of 1–3 ms, 0–5 kA. For detection purpose, a lamb wave wedge transducer is used with a frequency range of 10 MHz. This method has proven to be advantageous in terms of the cracks themselves behaving as a source of localized temporal stresses when subjected to the transient electromagnetic field. Also, it is found that there is a strong correlation of reflected ultrasonic waves with the externally applied energy of the electromagnetic field. In Ref. [51], the authors have developed a sensing technique using magnetic induction and the boundary element method for NDT of rails to detect flaws. This method is able to detect surface flaws in the rail head. Various flaws and sensor configurations had been simulated in MATLAB®. The relationship of the curve obtained is presented with the volume, shape, and position of the flaw. The effect of sensor distribution to best characterize the defect is also presented in this paper. The paper also presents a methodology to determine the position of the flaw based on the simulated results. The numerical solution is obtained for the magnetic field and the sensing coil voltages and these have been verified by taking measurements for different flaws. In Ref. [52], the authors have proposed a technique using electromagnetic acoustic transducers to gather information on defects based on moving average, cross-correlation, and self-correlation methods. After experimental verification, it has been proved that the received weak signals affected by noise could be improved by combining the previously mentioned methods. The signal received from the defect is preprocessed with the moving average method, which was found to be much more efficient than the cumulative average method. The fluctuations in the signal are successfully eliminated by the cross-correlation method. It has also been concluded that using a combination of both methods makes the signal much stronger than the one obtained by an individual method. The signal-to-noise ratio is improved by the combined methods to a greater extent. A flaw detection methodology using electromagnetic acoustic transducers is presented in Ref. [53]. This concentrates shear vertical waves to a line in a solid object. Shear waves are focused at constant frequency by subsequently changing the spacing of the meander-line coil. The amplitude of the signal reflected from the defect is measured after moving the EMAT on the upper surface. It was proved during the experiment that the accuracy of the line focusing EMAT is higher than that of an EMAT with constant spacing of the coil. It was also concluded that these meander-line-coil EMATs are more suitable to detect shallow defects of up to 0.05 mm depth. Jin et al. [54] have presented a nondestructive method to detect small cracks in thin-walled structures. A finite element modeling is also performed for verification purposes. The experiments performed show that AE signals are generated by the flaws vibrating when subjected to electromagnetic excitation along with electrodes. In Ref. [55], the authors have developed an NDT experimental procedure to detect the flaw location and size with high-speed and accuracy. The effect of depth of the flaw on transmitted wave intensity and the effect of flaw position on reflected wave intensity are also discussed in this paper. As presented, the depth of the cracks can be evaluated by microwaves below and above the cutoff frequency. As the signals below cutoff are weak, these can only be used for identifying crack position. This method has been verified to detect circumferential cracks inside metal pipes.

Laser-Based Detection Techniques.

Laser-based detection has been used successfully to detect flaws located in all sections of the rail, namely, the head, web, and foot. Laser-induced ultrasonic detection methods are by far the most successful approach [56,57] out of all these methods. A laser pulse having high optical power density is directed on to the rail surface. This produces a reactive stress in the ablative regime, which is normal to the surface. This ablation at the rail surface acts as an ultrasonic source that produces elastic perturbations (mechanical energy). This energy propagates through the rail material as ultrasonic waves. These ultrasonic waves are monitored with air-coupled transducers, which are oriented to guarantee complete inspection of the entire rail section. The detection of the laser-induced ultrasound waves is done using laser interferometer in Ref. [58]. A laser nonlinear wave modulation spectroscopy technique, which has been developed and presented in Ref. [59] for fatigue crack inspection, is also based on the laser-induced ultrasound waves. However, there is no clear evidence of the method being directly used for rail flaw detection. Work presented in Refs. [60] and [61] has been designed only to detect surface defects of specimens using the same principle. Generation of Rayleigh waves by means of a focused line laser source and their detection for the purpose of identifying surface defects is demonstrated in Fig. 9.

Guided Wave Principle-Based Detection Techniques.

Flaw detection using the guided wave principle is based on the fact that the rail functions as a wave guide for the acoustic signals of the ultrasound range. More specifically, the guided wave effect of ultrasound propagation in rail appears at frequencies lower than 200 kHz. Flaw detection methods based on the guided wave principle can be classified into two categories. They are the acoustic guided wave method and the laser-induced ultrasonic guided wave method. Reported work using these methods will be summarized later in this section of the paper. Underlying principles behind guided wave generation, propagation, and postsignal processing associated will be explained first.

The following key points can be listed in relation to the travel of guided waves inside rails [62]:

  1. (1)Due to the complex structure of a rail, which has three acoustically connected wave guides, namely, the head, web and foot, a great number of different core waves with different phase and group velocities can propagate inside the rail.
  2. (2)Interestingly, the acoustic connection between the head, web, and foot of a rail is relatively low. Therefore, the wave energy propagating along one of these wave guides almost does not penetrate to another. Based on this phenomenon, there are two important conclusions:
    • A defect in one of the rail sections, for example in the web, does not influence the ultrasound propagation in other sections, for example in the rail head.

    • Fastening of rails causes high levels of attenuation of ultrasound only in the foot of the rail, but does not influence propagation in the rail web and head.

  3. (3)Perpendicular oriented defects give higher reflected signals than do parallel oriented signals of the same size. For example, a perpendicularly oriented crack with an area of about 10% of the cross section of the particular rail section (i.e., head, web or foot) can be detected from a distance of up to 30 m from where it is located. This is true even for flaws in the foot of the rail.

The underlying physical principle used in this technique is the ability of the rail to function as a wave guide. The specific feature used here is the reflection property of ultrasound waves traveling inside the rail. Guided wave-based flaw detection can have a couple of different approaches depending on the way the ultrasound waves are generated in the rail, and also depending on the mechanism used to sense the waves in the rail for the purpose of flaw detection. Some reported approaches need permanent physical contact of the signal injecting and sensing mechanisms with the rail. Some other approaches do not need such physical contact, which makes them suitable for moving vehicle flaw detection system. Signal processing technologies and strategies used in the detection process of rail flaws can also vary from one approach to the other.

Manual Mechanical Excitation.

The work in Ref. [62] presents manual excitation by using a hammer to create the acoustic excitation, which triggers the ultrasound waves that propagate in either direction from the point of excitation. The flaw detector consists of an electronic unit with graphic display and a searching unit—antenna array. The operator has to manually place it on the rail. The antenna array consists of 12 ultrasonic transducers with dry point contact and an electronic controlling board.

Postprocessing Used in Guided Wave-Based Detection Methodologies.

The methodology in Ref. [63] is the time–frequency coherence function associated with continuous wavelet transform. In this approach, a wave propagation-based damage detection methodology has been developed in three steps. In the first step, the presence of damage on the structure is assessed. In the second step, the arrival time of the reflected wave (or echo) is estimated. In the third step, the damage location is estimated through a simplified ray-tracing algorithm.

Acoustic Guided Wave Method.

Use of ultrasonic guided wave-based detection has been reported not only for rail but also for other applications. An ultrasonic pulser/receiver unit has been used in Ref. [64], which is not in direct contact with the test piece for internal crack detection in thin metal plates. An ultrasonic transducer ring encircling a pipe is used in Ref. [65] to detect defects. The use of ultrasonic guided waves in NDT in general applications and different types of guided waves used and their applications are presented in Ref. [66].

Ultrasonic guided waves within a range of 60–280 kHz are used in Ref. [67] to detect transverse cracks in rails with shelling. Shelling takes place as a result of wheel to rail Hertzian contact stresses that lead to surface and subsurface defects. These defects are a result of high stresses below the surface of the rail. This work confirms that the guided wave method can detect shelling by employing the proper mode and frequency. It also confirms that the guided wave approach can also detect transverse cracking under shelling by selecting a mode and frequency insensitive to the shelling, but sensitive to transverse cracking. It must be noted here that the defect detection in this work concentrates only on the rail head.

Long range guided wave monitoring for rails flaws is a widely used technique. An automated detection method that uses long range guided wave together with a combination of power spectral density, short time Fourier transform, and wavelet transform for feature extraction is presented in Ref. [68]. The method is successful for detecting flaws in the rail foot. However, fixed sensors on the rail base are required. As such, the method is not suitable for mobile applications. A long range detection method presented in Ref. [69] is based on the principle that acoustic emissions or transient elastic waves are generated when there is any sudden release of strain energy within a material such as a crack propagating up from the foot of a rail under excessive tensile load. A portion of the acoustic energy released in this way propagates over long distances as guided waves through the rail. If a rail is monitored for acoustic emission events at two or more points in a length of rail, then the originating location of the acoustic emission can be determined by time-of-flight techniques. This approach is proven only for stationary monitoring applications. Another long range guided wave monitoring system for detecting rail breaks by using transmit and receive transducers permanently installed with a spacing of approximately 1 km apart is reported in Refs. [70] and [71] with no clear evidence on flaw detection. Interrelated research on finite element modeling of guided wave testing of rails [72], wave modes that can be utilized for guided wave detection in rails [73], and application of long range ultrasonic testing for flaw detection on hard to access areas of rails [74] detail other approaches suitable for stationary applications. The method presented has the capability of detecting flaws in the rail foot. However, it needs sensors mounted on the rail foot. Another stationary application with permanently mounted sensors on the rail to detect flaws using the ultrasonic guided wave principle is reported in Ref. [75]. The method uses an intelligent detection methodology consisting of four modules. The first of the four, namely, the preprocessing module filters the acquired signals and they are normalized. Next module does feature extraction from the normalized waveforms. Some of these features to mention are time domain (such as maximum, minimum, and average), power spectral density (such as peak power frequency, median power frequency, and spectral edge frequency), discrete Fourier transform, and wavelet transform. Once different kinds of features are extracted from the normalized signals inside the feature extraction module, the third feature selection module carries out a feature selection based on a fuzzy complementary criterion algorithm together with the generation of an informative and nonredundant feature subset. Finally, the fourth module classification carries out the flaw classification using a kernel-based support vector machine. In Ref. [76], an ultrasonic guided wave health monitoring technique for heavy duty rails is presented. The transmitter and receiver of the signals are permanently mounted on the rail placed 1 km apart. The technique was proven to be reliable in detecting faults, using an array of transducers to monitor and control the direction of propagation of waves. A thermite weld 790 m away from the transducer could also be detected with this method. It was feasible to detect cracks up to 500 m either side of the transducer array. In another approach not related to rail reported in Ref. [77], a guided wave-based technique to detect internal cracks in a hybrid structured layered plate is presented. A semifinite element analysis is carried out to select the appropriate mode and frequency from the dispersion curves to detect internal defects. The paper also states that, due to impedance mismatch, the conventional scanning techniques are not suitable to detect internal defects. Two angle beam transducers were used to generate guided waves, with incident angle of guided wave wedges as 35 deg with short distance angle beam transducer scanning. It was observed that 450 kHz A2 mode is suitable for detecting internal defects in a layered structure. In Ref. [78], the authors presented a guided wave detection technique using numerical and phase focusing methods. For phase focusing of lamb waves, two methods have been proposed—synchronizing time of flight of wave packet or at defect position, the phase of the wave packet can be synchronized. The wave mode A1 is excited at 780 kHz, four cycle tone bursts and spatial sampling of 1.27 mm. The B Scan results obtained from the experiment confirm that the phase and time of flight of echoes can be described with the help of a unique incidence angle. A technique to discriminate between critical and tolerable RCF in the rail surface is presented in Ref. [79]. Ultrasonic surface waves at a frequency of 200 kHz are used for the excitation purpose, which can travel long distances on the rail head. To remove the unwanted signals reflected from the rail surface, a spatial averaging technique is used. An eight-element transducer array is used with center frequency of 200 kHz and angled at 30 deg for generation of waves. The signal consisted of five cycle Hann windowed tone bursts with center frequency of 200 kHz. Through the experiment performed, it was confirmed that this method can distinguish between defective and defect free areas. This method was also found suitable to discriminate between critical and tolerable fatigue cracks on the rail with their locations.

Guided wave inspection of the bottom edge of the rail is the topic of Ref. [80]. It states that rail inspection in Japan has been carried out with inspection cars that move at a speed of 40 km/h and measure ultrasonic echoes with wheel probes and sliding contact transducers. However, it clearly indicates that those inspection cars are incapable of detecting rail foot flaws. In this study, guided wave modes vibrating largely at the bottom edges of a rail are identified using software for obtaining dispersion curves and wave structures. Then, echoes from a defect at a bottom edge of a rail are detected using these modes. Laser interferometry is used to measure the vibration of the rail and hence the method, as depicted in Fig. 10, would not be suitable for mounting on a rail inspection car.

Laser-Induced Ultrasonic Guided Wave Method.

Depending on the way the ultrasonic guided waves are originated inside the rails, it is possible to have a separate classification of guided wave detection termed “laser-induced guided wave detection.” Unlike other ultrasonic detection techniques, laser-induced guided wave detection is said to be a contactless method of examination [81]. The method presented in that work uses wavelet transform techniques for detection and it is based on the assumption that any damage present inside the rail alters the original wave propagation pattern. Verification of the method has been undertaken using wave patterns generated by means of a wavelet spectral finite element model. As such, no conclusions can be made on the practical implementation of the method as well as its applicability for rail foot flaw detection.

Another important work carried out by the U.S. Department of Transport that is worth referring to concerns high-speed rail defect detection [82,83]. The method uses the laser-induced guided wave principle and the reflected waves from rails defects. These waves are sensed using air-coupled sensors, which are not in contact and are placed ahead of the point of excitation of the rail head by the laser beam as shown in Fig. 11. This work also confirms the fact that all data acquisition and processing can be done using commercially available data acquisition and processing software and hardware solutions such as labview, which is a proven platform for research within research communities [83]. The system overview can be seen in Fig. 12 and the system performance can be further enhanced by placing an array of sensors as shown in Fig. 13 [83].

In Ref. [84], the authors have cited the use of a Q Switched neodymium yttrium aluminum garnet (Nd:YAG) laser for the detection of rail flaws. Laser pulse operating at a wavelength of 1064 nm with a pulse width of 4–7 ns and a beam diameter of 6 mm has been used. The power of the laser beam was adjustable between 40 mJ and 400 mJ. For the purpose of detection of ultrasonic waves, air-coupled transducers were used, which were capable of detecting frequencies between 50 kHz and 2 MHz. The work in Refs. [85] and [86] also uses similar laser excitation where the laser is filtered to operate in the thermoelastic regime. Aindow et al. [87] and Kromine et al. [88] have also taken a similar approach. A laser beam of rectangular cross section having a width of 0.4 mm and a length of 5 mm has been used in Ref. [89] to generate ultrasound waves. In Ref. [90], the authors propose the use of pulsed lasers for studying propagation of ultrasonic waves in solids and composite materials. A piezoelectric air-coupled transducer of bandwidth 1 MHz and an active area of 8 mm has been used in Refs. [91] and [92] for detection purpose together with the excitation being from an Nd:YAG pulsed laser of 1064 nm wavelength and 12 ns pulse duration having 82 mJ of energy. A slightly different approach of detecting vibrations using a laser Doppler vibrometer with a frequency bandwidth of 200 MHz while the sample is being excited with a similar laser sources is presented in Ref. [93]. In Ref. [94], the authors have derived an electromechanical coupling model of a piezoelectric ultrasonic transducer. Resonance frequency and vibration shapes had been measured with a scanning laser Doppler vibrometer. A similar laser interferometer approach is presented in Ref. [95] also. A scanning technique for curved surfaces using a laser Doppler vibrometer and a robot arm is presented in Ref. [96]. The results presented in Refs. [97104] have similar NDT approaches and it is worth mentioning here that those publications also provide comprehensive practical information on the specifications of the lasers and ultrasound equipment used in their research. The research published in Ref. [105] focuses on the state-of-the-art technologies available for NDT of rails at high speeds. A line source of Nd:YAG pulsed laser at 1064 nm wavelength and pulse duration of 8 ns has been used. This source generates ultrasound waves at frequencies of a few tens of MHz. Air coupled transducers are used for the detection with broadband response from 0 to 2 MHz. Signal analysis presented in that paper indicate that most of the energy is in the range of 100–700 kHz, with a dominating frequency of 200 kHz from which specific frequencies of 210 kHz, 425 kHz, and 600 kHz were used to find the crack depths. Also, it was analyzed that cracks of 5 mm and 7 mm depths reflect energy mostly at 200 kHz. Cracks of 1 mm depth reflect energy at 600 kHz.

Another key observation when it comes to laser excitation of rail is the difference in the excitation regions depending on the approaches the researchers have taken. As an example, the work in Ref. [106] uses a Q-switched Nd:YAG laser source with a wavelength of 1064 nm with a pulse duration of 10 ns and maximum energy of 200 mJ/s for generating ultrasonic waves in the ablation region. On the other hand Pan et al. [100] presents work undertaken on both the ablation region and the thermoelastic region by changing the laser excitation. Monchalin [107] have presented various principles of generation and detection of ultrasound. For example, generation of ultrasound waves in the thermoelastic region and generation by ablation or vaporization have been presented. Detection techniques such as Fabry–Perot interferometer, two wave interferometer, and detection laser have also been presented in this work. Another key observation is the ability of the laser-excited guided waves to be used for thermography imaging-based detection of the flaws as presented in Ref. [108]. Emphasis on signal processing aspects of the captured ultrasound waves by the sensors is the contribution in Ref. [109]. Finally, the book [110] can be strongly recommended as a good starting point for anyone wanting to initiate research in this area.

At this stage, it is worthwhile to focus attention on already reported reviews of rail flaw detection as well as rail breakage detection methods. A review done for CRC for Rail Innovation titled “High-speed detection of broken rails, rail cracks and surface faults” [111] details different types of rail breaks and available detection techniques. This work also lists several NDT methods including acoustic emissions and ultrasonic methods, magnetic field methods, radiography, eddy current techniques, thermal field methods, plus use of dye penetrant and fiber optic sensors of various kinds as methods reported for rail breakage detection.

Rail flaw detection technology that existed almost a decade ago is reviewed in Ref. [112], with particular emphasis on ultrasonic induction-based hi-rail vehicle projects that took place in North America and Europe. They are based on inspection cars that operate at speeds of up to 32 km/h. The main focus is on detecting flaws in the rail head with both ultrasonic and eddy current injection from the top of the rail head.

A comparison of eddy current-based detection and ultrasonic-based detection is presented in Ref. [113] together with some work on manual inspection systems used on different sections on the rail. It also confirms that eddy current-based detection is stronger in detecting flaws closer to the surface, while ultrasonic-based detection performs better for flaws located deeper in the rail as shown in Fig. 14. Table 2, extracted from Ref. [113], shows a comparison of the performance between the two methods.

Another review published in 2007 related to the famous Interail FP7 project funded by the European Union highlights ultrasonic phased arrays and alternating current methods [114].

Alternating current field measurement sensors, guided wave systems, laser ultrasonics, EMATs, and acoustic emissions as the detection techniques being researched in 2012 are reported on in publication [115]. It continues to reveal that the target of the Interail project is to enable the fast and reliable inspection of rail tracks at speeds up to 320 km/h depending on the system's mode of operation. The INTERAIL system is supposed to combine the use of automated visual inspection with ACFM and ultrasound testing probes into a single high-speed inspection vehicle. However, one of the most recent publications in late 2014 relating to the same Interail FP7 project reveals that they have only been able to develop an ACFM-based detection algorithm, which has been tested at speeds up to 48 km/h [115]. Yet, there is no evidence regarding rail foot flaw detection capability.

Another report based on research carried out in Sweden and Queensland, Australia detailing different rail defects and their propagation mechanisms together with very minor remarks of eddy current-based detection is worth mentioning here [116]. However, it must be noted that the emphasis in this work also is on the rail head.

Review on Commercially Available Solutions.

Another report worth mentioning summarizes several commercially available solutions, the technologies used in them, and their detection capabilities [117,118]. An extract of the commercial solutions listed in that report is given in Table 3.

The purpose of this section of the review paper is to present the big picture of rail flaw detection. Since rail flaws can be located on the surface as well as in the interior of the rail, it is obvious that visual inspection is not sufficient. In order to generate a signal that will contain the information about the presence of flaws, the rail under inspection must be excited using some appropriate mechanism. This excitation will generate some form of signal that will contain the information about the flaws inside the rail under inspection. The next step of the detection process is to sense this signal that contains information about the flaw. This is done by means of a suitable sensor. Once the signal is sensed, some advanced signal processing is essential for the purpose of extracting information about the rail flaws present out of the captured signal. This process is demonstrated in Fig. 15.

When the flaw detection process is looked upon in this manner, it is possible to classify all the flaw detection methods summarized in Sec. 2 of this review paper by looking at the excitation mechanism, signal detected, and signal detection mechanism used in each of those methods. Figure 16 graphically presents this global classification. It must be noted here that the classification does not take into account the signal processing methodologies used in different approaches since it is a very complicated task to classify modern signal processing methodologies.

The graphical illustration on the classification of rail flaw detection methodologies in Fig. 16 enables a better understanding of the various detection methods available as well as the relationships between various excitation methods and detection signals. As an example, it can be seen from Fig. 16 that ultrasound plays multiple roles. Ultrasound is one of the excitation methods for infrared imaging. It does the same in the case of laser interferometer-based detection, which is classified under ultrasonic guided wave methods. Interestingly, ultrasonic signals are the detection signals in the case of ultrasonic guided wave-based detection methodologies as well as ultrasonic detection method; the only difference is that the propagation of the ultrasonic signal in the latter case is normal to the rail surface. Another important point to highlight is the possibility that more than one excitation method can be used to generate the same detection signal inside the rail. One good example for this is infrared imaging, where there are three different excitation methods to change the temperature profile in the neighborhood of a rail flaw.

The purpose of this section of the paper is to summarize some of the key findings and technical details in relation to flaw detection methods analyzed in this paper. For each of the analyzed techniques, the capability to detect critical rail defects identified and summarized in the introduction is presented. The stage of application of each method in real rail industry is also presented. This information is given in Table 4. Another summary presented in this section is on the possibility to perform test without stationery instruments, time required to perform the tests and where possible, the ability to measure a defect of a certain size. This information is given in Table 5. The information is summarized in tabular form so that a comparative study is possible. For classification purpose, the global classification presented in Fig. 16 is used.

With the information presented in Tables 35, the following key observation can be highlighted as the major findings of this review. One observation is, majority of the commercial rail flaw detection solutions are based around ultrasound detection while, EMATs and eddy current-based detection have also been used in industrial solutions. In a few commercial products, combined detection strategies are used such as eddy current detection and ultrasonic detection. In case of detection systems mounted on rail cars, inspection speeds ranging from 32 km/h up to 100 km/h are available. Most of these methods are capable of detecting rail head defects and web defects in certain cases. No commercial solution has been reported on rail foot flaw detection.

With obvious reasons such as the need for faster detection capability, moving vehicle rail flaw detection solutions are preferred by industry. This review shows that still there is a lot of research that can be done to further improve the performance of moving vehicle rail flaw detection systems. The infrared thermography, pulsed eddy current method, and laser-induced guided wave method can be nominated as the detection methods that have higher potential in providing high-speed rail flaw detection solutions in the future.

The paper reviewed a broad range of rail flaw detection techniques reported so far in the scientific literature. It also summarized the rail flaw detection techniques used in several commercially available solutions and their capabilities. Most importantly, the paper presented a global classification of rail flaw detection techniques looking into the excitation and detection signals used by each of the reported flaw detection methods. A complete review of rail flaw detection methodologies of this nature has not been reported in the literature and hence it is believed that this work will provide a good starting platform for several researchers beginning to work in this area in future.

This work was supported by the Australasian Centre for Rail Innovation (ACRI) [HH1, MOVING VEHICLE RAIL FOOT FLAW DETECTION]. The authors also acknowledge the support of the Centre for Railway Engineering at Central Queensland University and the industry partners that have contributed to this project.

  • Australasian Centre for Rail Innovation (ACRI).

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Copyright © 2018 by ASME
Topics: Flaw detection , Rails , Waves
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Figures

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Fig. 1

Rail/wheel contact and the associated stress distribution [2]

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Fig. 2

Schematic diagram of the IR inspection system for fatigue cracks

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Fig. 3

Top view of a PEC probe aligned in the direction of magnetic induction flux [12]

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Fig. 4

Loading of rail steel sample during fatigue testing [19]

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Fig. 5

Local immersion probe used for surface wave excitation [33]

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Fig. 6

Definition of the field directions and coordinate system used in ACFM [35]

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Fig. 7

Primary and auxiliary inputs of the ANC filter [44]

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Fig. 8

Side view of EMAT [48]

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Fig. 9

(a) Laser-based generation of Rayleigh waves and detection and (b) focusing mechanism of the generated laser into a line source [60]

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Fig. 10

Measurement system based on laser interferometer scanning

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Fig. 11

Rail defect detection by ultrasonic guided waves excited by a laser and detected by an array of air-coupled sensors [83]

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Fig. 12

Hardware layout of the rail defect detection prototype [83]

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Fig. 13

Defect detection scheme in “transmission mode” with a pair of air-coupled sensors [83]

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Fig. 14

Variation of inspection areas covered by eddy current and ultrasonic-based detection techniques [113]

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Fig. 15

Typical rail flaw detection process

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Fig. 16

Global classification of rail flaw detection methodologies

Tables

Table Grahic Jump Location
Table 1 Characterization of rail defects
Table Grahic Jump Location
Table 2 Comparison between eddy current and ultrasonic inspection [113]
Table Grahic Jump Location
Table 3 Commercially available rail flaw detection solutions [117]
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Table 4 Comparison of detection methods in terms of capability and stage of development
Table Grahic Jump Location
Table 5 Comparison of performance of detection methods

Errata

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