LONG RANGE INSPECTION OF RAIL USING GUIDED WAVES
Paul Wilcox1'2, Brian Pavlakovic2, Mark Evans2, Keith Vine2, Peter Cawley3, Michael
Lowe3 and David Alleyne2
1
Department of Mechanical Engineering, University of Bristol, Bristol, BS8 1TR, UK.
Guided Ultrasonics Ltd, 17 Doverbeck
Doverbe Close, Ravenshead, Nottingham, NG15 9ER, UK.
3
Department of Mechanical Enginee;
Engineering, Imperial College of Science, Technology and
Department
Medicine, London, SW7 2BX, UK.
2
ABSTRACT. Low frequency guided acoustic waves will propagate many tens of metres along a rail.
The guided wave modes that can exist in a rail are found using a 2-dimensional finite element (FE)
technique and their interaction with a variety of features and defects is investigated with 3-dimensional
time-marching FE models. Results obtained from a prototype testing system are presented and
excellent agreement is obtained with FE predictions. Good sensitivity to transverse defects and defects
at alumino-thermic welds is demonstrated.
INTRODUCTION
Conventional Ultrasonic Inspection of Rail
Ultrasonic inspection systems that operate in the megahertz range have been used for
many years for the in-service testing of rail [1]. Two specific areas that can present
significant challenges for ultrasonic testing as currently deployed are the detection of
smooth transverse-vertical defects and the volumetric examination of alumino-thermic
welds. These two areas are of great importance as 39.5 % of rail breaks on the UK rail
network operated by Railtrack pic. were attributed to transverse-vertical defects and a
further 22.4 % were caused by faults at alumino-thermic welds [2].
Traditional ultrasonic techniques make use of transducers operating in pulse-echo
mode that are typically applied at 0° (normal incidence) and 70° to the running surface of
the rail on the center line. The normal incidence transducer enables the depth of the rail to
be determined and will also detect inclusions, horizontal cracks etc. The 70° transducer is
designed to detect cracks running in the transverse direction and it is ideally suited to
detecting cracks in a plane at 20° to the vertical and many cracks do run at approximately
this angle. There will also be some reflection from truly vertical cracks running normal to
the axis of the rail, particularly if they have rough surfaces. A tandem arrangement of a pair
of transducers operating in pitch-catch mode may be better suited for the detection of
transverse cracks, but it is more complicated to deploy. A serious problem with either
method for detecting transverse/vertical defects is that the wave path is often blocked by
cracks running close to and almost parallel to the running surface of the rail. In the case of
alumino-thermic welds, the large material grain size strongly scatters ultrasonic waves at
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© 2003 American Institute of Physics 0-7354-0117-9/03/S20.00
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the frequencies that are used giving rise to high attenuation and reflected signals that are
very difficult to interpret.
Guided Wave Inspection of Rail
An alternative inspection strategy is to use guided acoustic waves that can propagate
many metres along a rail. When such a guided wave is incident on a feature in the rail some
energy is reflected back along the rail, hence guided waves are potentially very attractive
for rapidly inspecting long lengths of rail.
Guided waves tend to be sensitive to the projected area of a feature or defect onto the
cross sectional plane of the rail. For this reason they are ideal for detecting defects such as
transverse vertical cracks of the type that can lead to catastrophic failure, but they are
relatively insensitive to less critical defects such as cracks running parallel to the rail. A
further advantage is that at the frequencies used, material attenuation due to grain boundary
scattering is very low and hence alumino-thermic weld material can be readily penetrated
and tested. These benefits make guided wave testing very attractive. The complications of
using guided waves are that there are many different types (or modes) of guided waves that
can exist, and in general these modes have different, and frequency dependent, velocities.
Without careful design of the transduction system and a full understanding of guided wave
mode behavior, the superposition of multiple modes all traveling at different velocities will
result in data that is very difficult to interpret. The key to an effective and practical test is
the determination of the particular guided wave modes that are required for the test and the
preferential generation and detection of these modes relative to the other unwanted modes
[3]. In this paper, the development of a prototype guided wave rail inspection system is
described, beginning with a summary of the theoretical study of guided wave behavior in
rail. This is followed by an examination of some of the experimental results that have been
obtained to date.
DEVELOPMENT OF PROTOTYPE RAIL TESTING SYSTEM
Prediction of Modal Properties of Guided Waves in Rail
Analytical models exist for the exact calculation of guided wave behavior in
structures with simple cross sectional geometry such as plates and pipes [4]. However, no
such exact model exists for complex profiles such as a rail. Instead, a two-dimensional
(2D) finite element (FE) method has to be employed to predict the modes-shapes and
guided wave characteristics for structures with complex cross sections such as rail [5,6].
Recently, other workers have obtained similar results using a resonant 3D FE model [7].
Figure l(a) shows a portion of the predicted phase velocity dispersion curves for
BS113A type rail. At the upper frequency limit of 50kHz shown in the figure there are
around 20 modes present. Figures l(b-c) show the exaggerated mode shapes for two of the
guided wave modes that can exist in this frequency range. Important practical information
about the use of a guided wave mode for testing can be inferred or calculated from its mode
shape. This information includes for instance, the optimum location and orientation of
transducers for excitation and detection and the likely sensitivity to defects in various parts
of the rail.
Selection of Guided Wave Modes for Long Range Inspection of Rail
Although the 2D FE model provides an essential starting point for designing a guided
wave testing system, there is much more information required in order to determine which
mode or modes to use and at which frequency.
237
(a)
10
(b)
i
(c)
10
20
30
Frequency (kHz)
40
50
FIGURE 1.
(a) Phase velocity dispersion curves for guided wave modes in BS113A type rail and some
examples of guided wave mode shapes for (b) a mode with energy concentrated in the foot and (c) a mode
with energy concentrated in the web. Note that the displacements are greatly exaggerated in these diagrams.
The 2D FE model does not include the fact that real in-service rails are clipped to
sleepers at regular intervals, and that these clips provide a major mechanism for energy
dissipation and hence attenuation of guided wave energy. For this reason, an extensive
program of experimental testing has also been used to identify guided wave modes and
operating frequencies that are suitable for long-range testing.
Transducer Array Design
A mode is most efficiently excited when the harmonic force applied by a transducer
is well coupled to the displacement associated with the mode and this information is
provided by the mode shape from the 2D FE model [6]. This means that transducers should
be placed at points on the surface of the rail where the displacement in the mode shape is
high and in the same direction as the polarization of the transducer. The situation is
identical for efficient reception.
For practical testing, simply exciting and detecting a mode efficiently is not enough;
it is additionally necessary to suppress information from unwanted modes. For reasons that
are addressed below, it is also desirable to be able to utilize a number of different modes. It
has therefore been decided to employ an array of independently controlled transducer
elements as this represents the most flexible transduction system. Careful consideration of
the mode shapes of the guided wave modes of interest has enabled the location of
transducer elements in the array around the perimeter of the rail cross section to be
optimized. Furthermore, it is necessary to be directionally selective in the excitation and
detection of any guided wave mode. For this reason it is necessary for the array to also
have a distribution of transducer elements along the length of the rail, as well as around it.
When in use, the transducers in the array are pressed onto the surface of the rail and
because of the relatively low operating frequency, they do not need liquid couplant. The
deployment of transducers to points all around the perimeter of a rail (excluding the
underside) has required the development of a complex clamping system that utilizes a
combined mechanical and pneumatic actuation system. A photograph of the prototype
system is shown in Figure 2.
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USE OF GUIDED WAVES TO DETECT DEFECTS IN RAIL
Prediction of Reflection Coefficients from Defects and Features
A mode is most sensitive to defects at positions in the rail cross section where the
energy in the mode shape is most concentrated. For example, the mode shown in
Figure l(b) will be most sensitive to defects in the toe whereas the mode shown in
Figure l(c) will be most sensitive to defects in the web. Mode shapes provide qualitative
information on which modes will be sensitive to certain types of defects but they cannot
provide quantitative data. To obtain quantitative reflection coefficients, a three-dimensional
(3D) time-marching FE model needs to be used.
When a uni-modal guided wave signal is incident on a defect (or other feature) a
portion of the energy is reflected but the reflected energy is not uni-modal. In general,
mode conversion occurs and the reflected energy is partitioned between several guided
wave modes.
To fully model the interaction of guided waves with a defect it is necessary to obtain
reflection coefficient data for each combination of incident and reflected mode. For each
feature or defect considered, the same 3D FE model must be run separately for each
incident mode of interest. In each run, special signal processing techniques are applied to
decompose the results into contributions from each reflected mode of interest. In this way a
matrix of reflection coefficients for each incident and reflected mode combination may be
constructed for each feature or defect. The defect and feature geometries that have been
modeled are shown in Figure 3. In all cases, the 3D FE predictions have been validated
with experimental measurements.
Comparison of Predicted and Experimental Reflection Coefficient Maps
A convenient way of viewing the information in a reflection coefficient matrix from a
particular defect or feature is to make a color or grayscale map of the amplitude of the
elements. An example refection coefficient map is shown in Figure 4.
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FIGURE 2.
Photograph of prototype rail testing array.
239
Head
Head
Web
Asymmetric
Symmetri
Gauge corner
Foot
Head internal
FIGURE 3.
Defect geometries that have been modeled.
Symmetric •<—>• Anti-symmetric
mode conversion
Reflected mode
FIGURE 4.
Example reflection coefficient map showing symmetric and anti-symmetric modes.
The order of the transmitted and received modes is the same and it is arranged so that
the anti-symmetric (with respect to the center line of the rail) modes are first and the
symmetric modes are second. A symmetric feature will not cause mode conversion
between symmetric and anti-symmetric modes, hence certain portions of the reflection
coefficient map will always be zero for such features. However, non-symmetric features
will cause mode conversion between symmetric and anti-symmetric modes and are hence
readily identified by the appearance of non-zero elements in these locations. It has been
found that the ratio between elements in a reflection coefficient matrix for one particular
type of reflector (e.g. a symmetrical transverse head crack) tends to remain relatively
uniform as the size of the reflector is increased, although their absolute values increase.
The maps therefore provide a visual classification tool that enables various types of defect
and structural features to be distinguished and the amplitude of a map enables the severity
of a defect to be estimated.
The prototype guided wave rail inspection system has been tested on a number of rail
samples ranging from 5 to 20 m in length and containing a variety of artificially introduced
defects. Good agreement has been obtained between the predicted and measured reflection
coefficient maps and some examples are shown in Figure 5. Some key features should be
noted. Firstly, the reflection coefficient map for the end of a rail is dominated by the
elements in the leading diagonal showing that for this feature there is very little mode
conversion at all. Secondly, the effect of asymmetry on the reflection coefficient map for a
transverse crack in the head is clearly indicated by comparing Figures 5(b) and (c).
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incident Mode
FIGURE 5.
FE predictions of reflection coefficient maps for (a) end of rail; (b) symmetric transverse
crack in top of head; (c) transverse crack in side of head; (d) symmetric crack in centre of foot. The
equivalent experimentally obtained results are shown in (e), (f), (g) and (h).
Comparison of Predicted and Experimental Defect Reflection Coefficients
Various experimental measurements have been made on samples containing artificial
defects of progressively increasing severity. The results from these tests have confirmed the
predicted relationship between defect severity and the absolute values in a reflection
coefficient matrix. As an example, the predicted and measured reflection coefficient of a
particular mode as a function of the cross sectional area loss associated with a transverse
symmetrical head crack is shown in Figure 6.
Automated Feature Extraction
The result of a test is a series of A-scans (i.e. a function of reflected signal amplitude
vs. distance from the transducer array) that correspond to each of the possible transmitted
and received mode combinations. At any distance from the transducer array, the amplitude
of the relevant points in each of the A-scans can be assembled to form a reflection
coefficient matrix. Work has begun on creating an automatic algorithm for feature
extraction that compares the experimental data at every distance to a database of reflection
coefficient matrices obtained from 3D FE modeling. Preliminary results from this
algorithm are illustrated in Figure 7.
Experiment
O Finite element prediction
0.6
-
i 0.4
! 0.2
10
20
30
40
Cross sectional area loss (%)
50
FIGURE 6.
Comparison between experimental and FE predictions of reflection coefficient for a
symmetrical transverse head crack as a function of rail cross-sectional area loss.
241
(g)Foot(S)
(h) Foot (A)
5
10
15
20
Distance (m)
FIGURE 7.
(a) Schematic diagram of a rail specimen containing multiple features. The graphs (b-h)
show the extracted amplitudes (on a linear scale) of signals from the various feature types indicated. S and A
denote features that are symmetric or asymmetric with respect to the vertical centerline of the rail cross
section.
Figure 7(a) shows a schematic diagram (not to scale) of a test rail containing a
number of artificially introduced defects and an alumino-thermic weld containing a crack
in one side of the head. The plots (b-g) below are signals automatically extracted from a
single set of test data that correspond to various types of feature. It can be seen that signals
from the majority of the features are correctly classified, although there is duplication in
some cases. The extraction algorithm will be refined as more data is obtained.
USE OF GUIDED WAVES TO INSPECT ALUMINO-THERMIC WELDS
Possible defects at alumino-thermic welds include inclusions, porosity, lack of fusion
and hot tearing. The conventional ultrasonic test procedure for checking such welds is not
designed to penetrate the weld material itself, hence reliable detection of porosity or hot
tears within the weld is difficult. Guided waves are potentially sensitive to either of these
types of defect as well as lack of fusion because their low frequency means that they do not
suffer from significant attenuation in the weld material.
Using guided waves to detect defects hi the weld is more complex than in the case of
free rail since the extra metal at the weld (i.e. the weld cap) produces a significant
reflection of guided wave energy. A reflection coefficient map for a defect free weld is
shown in Figure 8(a). FE studies have indicated that uniformly distributed porosity is likely
to affect the amplitude but not the appearance of the map. Given that there is also likely to
be a significant variation in reflected amplitude from good welds due to variations in the
casting geometry, it is thought that uniform minor porosity will be difficult to detect at
welds.
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FIGURE 8.
Experimental obtained reflection coefficient maps for welds: (a) good weld; (b) weld with
crack (4 % cross sectional area loss) in side of head; (c) weld with crack (2 % cross sectional area loss) at end
of one toe; (d) weld with crack (4 % cross sectional area loss) at end of one toe.
Minor uniform porosity in itself is not likely to lead to a sudden catastrophic failure,
whereas fatigue cracks, possibly initiated by porosity may do so. The ability of guided
waves to detect cracks at welds has been investigated with FE and experimental studies.
Experimental measurements were made of the guided wave reflection coefficients from
transverse saw cuts of increasing depths cut into initially defect free alumino-thermic welds
in 20 m long rail samples. The saw cut locations investigated were (a) at the side of the
head, in the center of the weld material and (b) at the extremity of the toe at the interface
between the weld material and the rail. The weld containing the latter defect was tested
from both directions and negligible difference was observed in the results, although the
defect was on one face of the weld. The reflection coefficient maps for various crack
severities at the two locations are shown in Figure 8(b-d). It can be seen that even very
small cracks cause significant changes to the pattern of the reflection coefficient maps for a
weld, hence indicating that these may reliably detected in site conditions.
CONCLUSION
Guided waves offer an exciting means of rapidly screening long lengths of rail for
transverse defects. It has been shown that the problems associated with guided wave testing
in a multi-modal environment can be overcome through careful design of the transduction
system. Furthermore, the concept of taking advantage of multiple modes through the use of
reflection coefficient maps for feature identification has been demonstrated.
ACKNOWLEDGMENT
The authors would like to acknowledge the partial funding of this development work
by Railtrack pic., and in particular their engineering input which has enabled the project to
progress rapidly over the last 12 months. The authors would also like to express their
gratitude to Thermit Welding (UK) Ltd for providing access to alumino-thermic weld
specimens and providing their testing facilities.
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