DATA FUSION METHOD FOR THE OPTIMAL MIXING OF
MULTI-FREQUENCY EDDY CURRENT SIGNALS
Z. Liu, M.-S. Safizadeh, D. S. Forsyth, and B. A. Lepine
Structure, Material, and Propulsion Laboratory, Institute for Aerospace Research
National Research Council Canada, Montreal Rd 1191, Ottawa, ON K1A OR6,
Canada
ABSTRACT. Eddy current testing methods are commonly used in the inspection of aircraft
fuselage splice joints for corrosion and fatigue damage. However, the inspection of these
components suffers from the spurious effects introduced by liftoff, interlayer gaps, rivets, and
paint. To overcome these effects, multi-frequency eddy current testing has been proposed, but
has not been widely employed due to the difficulty of correctly mixing the different signals. In
this study, we investigated the potential of using a data fusion technique to combine the results of
different frequencies and quantify hidden corrosion in service-retired aircraft lap splice joints. An
updating mechanism based on a contextual Dempster-Shafer approach was used to combine
probability mass values from multiple sources. The final classification results were obtained by
making decisions based on the maximum belief of the fused results.
INTRODUCTION
Manual eddy current (ET) inspections are the most commonly used NDI
procedure in aircraft maintenance. Most often they are called for in the inspection of
components for fatigue cracking, but they have also shown potential for detection and
quantification of corrosion damage in the faying surfaces of fuselage splice joints. In
the case of splice joints, the ET signal is affected by changes in thickness of the joint
material, which is then assumed to be due to corrosion. The ET signal is also
confounded by a number of extraneous factors including variations in probe tilt,
probe-specimen liftoff, and interlayer gap to name a few. In order to eliminate some of
the extraneous factors, the mixing of multiple frequencies of ET has been proposed by
various authors (see for example [1,2]). However, these procedures have not been
adopted into common practice, at least in part due to the complexity of the procedures
and the sensitivity of analog signal "mixing". This paper presents results from multiple
frequency eddy current testing (MFECT) where the mixing is performed by computer
post-test using data fusion algorithms.
When multiple inspections provide complementary information about the
specimen, combining the data may facilitate the analysis or classification process.
Data fusion techniques provide a framework to fuse and integrate information from
CP657, Review of Quantitative Nondestructive Evaluation Vol. 22, ed. by D. O. Thompson and D. E. Chimenti
2003 American Institute of Physics 0-7354-0117-9
577
multiple sensors or sources. Dempster-Shafter (DS) theory is one of the data fusion
approaches that provide a mechanism to fuse information from multiple sources.
Fusing of MFECT data is not a new topic and some researchers presented various
fusion algorithms to achieve a better signal-to-noise ratio [3,4]. The use of DS rule to
fuse nondestructive testing (NDT) data was described in reference [5] by Gros. The
key difference between DS and other common data fusion methods is the assignment
of probability mass, which is the first and crucial step in the process. Unfortunately,
there is no common answer to the question. This remains an unsolved problem and
largely depends on the application itself.
To detect and characterize corrosion and fatigue damage in aging aircraft, in
the case of multi-layer riveted joints, the following metrics may be required: material
thickness loss by layer, corrosion pit size and distribution, pillowing deformation; and
crack size, location, and orientation [6,7]. It is not likely that one single NDT method
can characterize or quantify all these metrics. Because multiple NDT methods are
employed for these inspections, this raises the potential for applying data fusion
techniques to interpret multi-sensor data or quantify the results. At NRCC, the
following NDT methods are being used for detecting hidden corrosion in aging
aircraft: single/multiple frequency eddy current, pulsed eddy current, ultrasonic, and
two computer vision based systems: Edge of Light ™ and DSight ™ inspection.
In this paper, we present the application of a DS fusion rule to fuse MFECT
inspection data from Boeing 727 aircraft lap splice joints. A probability mass was
assigned to each pixel, and after applying the DS fusion rule a contextual process was
carried out to update fused results. The decision was made based in the maximum
belief of the updated results.
APPLYING DEMPSTER-SHAFER THEORY
The DS Method
The core of the DS method contains three aspects: the concept of probability
mass, belief function, and the updating mechanism [5,8]. The frame of discernment 6
is a finite set of propositions that are mutually exclusive and exhaustive. The power set
of 9 is 2° , and the elements of this set are all subsets of 9 . The mass function assigns
degree of belief across the set of all subsets of #, such that,
jn:2*->[o,l], mfa)=0
The quantity m(A) , known as basic probability assignment (BPA), is a real number
between zero and one. It represents the exact belief committed to A . A function "Bel" is
called a belief function if it satisfies the following conditions:
Jfe/:2'->[o,l], Bel fa) =0
Bel(A) = £ m(B) , for all A c 9
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(2)
Bel(A) is a real number between zero and one that represents a degree of support that all
the available evidence provides for A. Given a belief function Bel(A), the function
Dbt(A) = Bel(-iA) is called a doubt function and represents the total support for the
negation of a proposition. The plausibility function of A, denoted by P1(A), is written as
Pl(A) = l-Dbt(A).
We assume the current state of the system has the value 7^(4) assigned to all the
subsets of 0, and represents the total support from all the previous evidence. The
observation of a new distinct piece of evidence by a mass function m2, distributes a new
set of mass values m2(Aj) over the set of 29 . These new mass value m2 and the old values
ml are combined to produce updated values mu. The updating mechanism is performed by:
(3)
where mu is called the orthogonal sum and can be written as mu = ml ®m2. Formula (3) is
called the DS rule of combination. The crucial problem of using DS is how the mass
function distributes the mass values among the subsets of the frame of discernment.
Unfortunately, DS theory does not give the answer. The procedure largely depends on the
application.
The Procedure of PS-based Contextual Data Fusion
The procedure that was used for fusing MFECT data is given in Figure 1. To relate
the measurement value (voltage) to corrosion damage (material loss), one can use a
straightforward calibration approach. That is, given a measured voltage, find the material
loss corresponding to that measurement using a calibration curve. However, a measurement
value does not uniquely correspond to a material loss quantity, due to noise sources
affecting the measured signal. In this work, it is assumed that the actual material loss for a
measured value is normally distributed. While this has not been demonstrated for the
MFECT data considered herein, this assumption has been widely used for estimating the
probability of detection [9]. This has also been demonstrated to hold true for diverse NDE
methods [10] in a more general derivation.
M
M*:_
ECT 5.5 kHz
—h»i
ECT 17 IcHz
Data
Distribution
Fused Result
,.miM .,
W
2t
W
22
OS
Rule
Contextual
Process
^
,v
max ( « A )
.1,
mass value
FIGURE 1. The procedure to fuse MFECT data.
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^^^^^^^^M
One way to obtain this distribution is to use calibration specimens. In order for the
results to accurately model the range of results expected from inspection of actual inservice components, all the relevant variables must be included in the calibration
specimens. In the case of riveted lap splice joints, these variables include paint, probe tilt
and liftoff, and interlayer gap variations. Construction of these calibration specimens
quickly becomes a complex and arduous task. Thus, in these experiments, a section of an
actual Boeing 727 lap splice was used as prior knowledge to build the distribution maps.
The specimen was inspected with the MFECT technique described below, and then
destructively examined. After disassembly and cleaning, the individual layers were
thickness mapped using a radiographic technique developed at NRCC. The range of
measured values of thickness was divided into 100 data bins, with each data bin
corresponding to a modeled normal distribution curve that satisfies formula (1). From the
distribution map, the probability mass value is assigned.
Usually for DS methods the decision is made on the result of formula (3), but the
contextual information is not considered herein. A contextual process integrates the spatial
correlation between adjacent pixels in order to improve the classification results [11]. A
simple implementation of this process computes the average of a 3 by 3 block region:
'M4),
J
=I
>12(4),,,
y q=i-\ r=j-l
(4)
The contextual process can be iterated if the procedure generates newly labeled pixels.
Finally, each pixel is assigned to the type of corrosion with the maximum belief value.
EXPERIMENTS AND RESULTS
A MIZ 40A eddy current instrument was used in the experiment to drive a
sliding probe housing with adjacent coils in transmit/receive configuration. The probe
was excited at four discrete frequencies, -5.5 kHz, 8 kHz, 17kHz, and 30kHz,
simultaneously during one scan. The Winspect™ data acquisition software was used
to capture the inspection data. The subject specimen was a section of a service-retired
Boeing 727 lap joint. It consisted of two layers of nominal 0.045" thickness Al 2024T3, and a stringer of Al 7075-T6. The joint was fastened with three rivet rows of equal
1" spacing between rivets. A photograph of the specimen is shown in Figure 2 and the
inspection results are shown in Figure 3. After applying MFECT and other NDT
inspections, the specimen was disassembled, cleaned, and the individual layers
inspected with a radiographic technique to determine remaining thickness.
FIGURE 2. Photograph of a section of the Boeing 727 lap splice joint used for this study.
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FIGURE 3. MFECT inspection data at four frequencies. (The scale is in voltage.)
Note that only the imaginary parts of the signals were used in the DS analysis.
Since a conventional eddy current inspection procedure was used, the real component
of the signal is affected by varying probe liftoff, interlayer gap, and some other
factors. For this reason, phase information was not analyzed. Table 1 gives the relation
between the frequencies and penetration depths. In general, the 30 kHz and 17 kHz
inspections reveal the first layer's characteristics while the 8 kHz and 5.5 kHz
frequencies penetrate to the second layer.
TABLE 1. Inspection frequencies and penetration depths.
Frequency
(kHz)
30
17
8
5.5
(mm)
0.698
0.925
1.349
1.627
Penetration depth
(xlO'3 inch)
27.5
36.5
53
64
X-ray thickness
map: top (left)
and bottom
(right)
m^m
II II
Fusing 5,5 and
8kHz
for top
layer (left) and
bottom layer
Fusing 17 and
30kHz data for top
layer (left)
bottom layer
(right)
••0035
OJ1
tJtl
FIGURE 4. Image fusion results for section D. (Grayscale shows the thickness and the unit is inch.)
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For the procedure of mass function assignment, 20 classes of corrosion types
were defined according to the percentage of material loss from the nominal thickness.
The MFECT inspection of section D was used to generate the probability mass
functions, and the results of the DS fusion on section D are shown in Figure 4. It was
found that the areas near the rivets did not follow the same distribution as the other
measurements, and these pixels were excluded by applying a preprocessing step to the
MFECT images. The square blocks in the images show the positions of rivets and all
values in these areas are set to zero.
It is important to note that section D, which was used to generate the
distributions for the fusion rule, contained very little second layer corrosion. This is
one drawback of using service-retired specimens for "training"; it is difficult to know
if the desired range of corrosion on each layer is available in the specimen until
teardown. Furthermore, it is for this reason that the fusion method was not tested for
its ability to discriminate between multiple layer damage.
Figure 5 shows the results of section C. There is little corrosion on the bottom
layer. To evaluate the classification results, rate of classification is often adopted.
However, this criterion is not suitable for this application. For example, misclassifying
type 5 to type 6 will be treated the same as misclassifying type 5 to type 16. The
difference between these corrosion classes is the percentage of material loss.
Obviously, the former "misclassification" is better than the latter one. Figure 6 gives
the difference images between X-ray thickness map and fused results (thickness maps)
of section D and C. The mean errors for these two images are 0.0018 and 0.0019
respectively. This value is approximately 4% of the single layer thickness.
X-ray thickness map: top layer (left) and bottom Layer (right)
1:
»
Fusion results of 5.5 and 8kHz for top layer (left) and bottom layer (right)
JLJL
Fusion results of 17 and 30kHz for top layer (left) and bottom layer (right)
0.005
0.01
0.015-'
0.02
0,025
0.03 0.035
0.04 0.045
FIGURE 5. Image fusion results for section C. (Grayscale shows the thickness and the unit is inch.)
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FIGURE 6. The difference between X-ray thickness map and fused results of section D 1st layer (left)
and section C 1st layer (right).
DISCUSSION
When pixel level data fusion is considered, an affine transformation that
registers multi-sensor images should be performed in advance (see, for example [12]).
Fortunately, for MFECT techniques, the discrete frequencies can be captured in one
scan so the registration process can be omitted. However, to find the relationship
between the material loss and the voltage values of MFECT measurement, there is still
the need to match the X-ray thickness maps and MFECT images. The problem is that
X-ray thickness map is of much higher resolution. The resizing of X-ray map to fit
MFECT images may introduce error and it would be better to perform a low-pass
filtering before using it. Observing the material loss and MFECT measurement, we
cannot find any linear or near linear relation between them as might be derived from
using simple calibration specimens. This demonstrates that without dealing with the
effects of liftoff and interlayer gap, use of calibration data may not lead to a correct
evaluation result.
In our experiments, we used the distribution map of section D as a priori
knowledge, and tested the resulting algorithm using section C. This works well for
both section C and D; however, if there are too few pixels in certain data bins, the
derived distribution curves may be inaccurate. This will affect the mass function
assignment especially when the data to be evaluated has more pixels in this range. To
solve this problem, more data should be collected to build the distribution map.
The results for the first and second layers are obtained by fusing high and low
frequency pairs respectively. It is obvious that fusion of the higher frequency images
achieved a better result for the first layer. Any combination of low and high frequency
images does not improve the results for evaluating first layer corrosion. This implies
that data fusion does not assure good results. When complementary information is
available, an effective fusion operation will be helpful. The crucial step is the mass
function assignment. There are various approaches for this and it largely depends on
the application itself. The mass function will be more reliable if a data-driven and
objective process is employed.
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CONCLUSION
In this paper, a data fusion scheme based on Dempster-Shafter theory is
presented for fusing multi-frequency eddy current data. The results of multiple
frequency eddy current inspections of hidden corrosion in a multilayer lap joint are
quantified through this proposed approach. The results can then be used as the input to
structural analysis models.
Further improvement to the results will be attempted by obtaining more
"training" data from service retired specimens in order to better model the relationship
between the eddy current signals and the thickness of the layers of the joint. Other
NDI techniques could also be used, and results fused with the MFECT results for
improved quantification and reliability.
ACKNOWLEDGEMENTS
Funding of this work is provided by National Research Council Canada and
Department of National Defence Canada, AVRS.
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