ADVANCED LOCATION AND CHARACTERISATION OF DAMAGE IN
COMPLEX METALLIC STRUCTURES USING ACOUSTIC EMISSION
Dr. R. Pullin, Prof. K. M. Holford, Dr. S L. Evans, M. Baxter and J. J. Hensman*
School of Engineering, Cardiff University, The Parade, Cardiff, CF24 3AA, UK
*Dynamics Research Group, Department of Mechanical Engineering, University of Sheffield, Mappin
Street, Sheffield, S1 3JD, UK
[email protected], [email protected], [email protected], [email protected],
[email protected]
ABSTRACT
The current commercial approach to acoustic emission source location, called time-of arrival (TOA), requires knowledge of
the sensor position and an accurate measure of wavespeed. Furthermore TOA assumes a straight path of propagation
between the source and the sensor, which in complex geometries is rarely the case, whilst ambiguities and errors arise
due to the minimising of the number of sensors, premature triggering of timing measurements and dispersion of the wave.
In previous work, a novel solution for AE source location in geometrically complicated structures has been developed.
Delta-T mapping source location utilises an artificial source; differences in times of arrival from a number of locations are
recorded, to improve source location. The method does not require knowledge of the sensor position or wavespeed. This
work however only reported on the results from an artificial source.
In the current work a fatigue fracture was grown in a test specimen with a complex geometry and monitored using the
Delta-T mapping technique. The specimen was manufactured from 3 mm mild steel plate with a variety of holes to
interfere with direct wavepaths from source to sensor, and the specimen was loaded in uniaxial tension from 0.35 – 35 kN
until failure. In any investigation, once the position of a source is identified, a method of source characterisation is needed.
A common method is to examine the feature data of a signal (amplitude, energy, rise-time, counts etc.), but this can be
complex as it is only possible to visualise the data in two or three dimensions, such as amplitude against counts. However
using principal component analysis (PCA), data can be observed in a greater number of dimensions and clusters of data
with maximum separation can be displayed. With prior knowledge of cluster locations a source characterisation is
possible. A further technique, K-means clustering which assigns K-centres to a data set prior to assigning a signal to a Kgroup, has also been used to assess the results of the PCA.
An analysis of the results post failure demonstrated that the technique of Delta-T mapping showed approximately 50%
improvement in source location over the traditional TOA technique. An AE source location cluster analysis and sample
signals from the three main groups of activity were taken and assigned group numbers. A PCA of the sampled signals,
irrelevant of location, showed distinct clusters that separated the three groups. A K-means analysis of the same signals
showed a 90.1% agreement when compared with the PCA. This investigation demonstrated that the four techniques can
be developed into an appropriate method for on-line source location and characterisation of fatigue fractures in complex
metallic geometries.
Introduction
Before a new landing gear design is installed into a commercial aircraft, an airworthiness investigation must be completed.
Messier-Dowty are the world’s leaders in the design and manufacture of landing gear and routinely perform airworthy
investigations. Tests last approximately five years with 25% of that time assigned to periodic non-destructive testing
(NDT). A method utilising AE that can monitor the structure to limit periodic NDT testing is currently being investigated.
Landing gear modules are very complex, and predominantly metallic, structures, with numerous lugs, cutouts and
thickness changes that complicate the propagation paths between AE sources and sensors. In addition the environment in
which landing gear modules are fatigue tested are very high noise, with AE sources coming from friction between
components, structural vibration, bearings and actuator noise which complicates source identification.
This paper focuses on three techniques developed to overcome problems of complex geometry and noise, Delta-T
mapping for source location and for source characterisation principle component analysis (PCA) and K-means clustering
and one established technique, source location cluster analysis.
Source Location
Source location is an important feature of AE monitoring. If the location of an event is known, the number of possible
source mechanisms is reduced as only certain mechanisms are associated with particular geometric features and
conditions. For example, fretting will only occur at an interface between moving surfaces or areas of high stress
(determined using a finite element model) could be used to weight the possibility of a source being a fatigue fracture. In
addition, identifying the location of an AE event can allow other non-destructive evaluation (NDE) techniques, such as dye
penetrant or ultrasound, to be utilised.
Current source location techniques rely on the ability of several sensors to detect an AE event and locate the event using
simple triangulation methods based on time-of-arrival of the fastest propagating wave mode [1]. However these source
location methods are based on two assumptions:
•
•
Wavespeed remains constant from source to sensor. When considering plate waves, which are developed in
landing gear modules, the velocity of any particular is dependant on thickness of the plate it is travelling in. This is
well documented [2,3] and graphically demonstrated in dispersion curves [4].
That there is a direct wavepath between source and sensor. However, geometric features such as holes and lugs
can dramatically alter the wavepath. Indirect paths may include reflection, refraction diffraction and paths
depending on the geometry of the component.
Both these assumptions introduce errors. In simple cases, these problems can be overcome with expert knowledge,
assessment of wavepaths and intelligent sensor location. However these are estimations and cannot provide accurate
results. Delta-T mapping source location overcomes these assumptions to provide a more accurate result and does not
involve any in depth theoretical calculations that are associated with single sensor source location [3] and energy based
spatial location [5]. A more detailed description of the Delta-T mapping technique can be found in [6] however a summary
of the technique is provided below in five steps :
Determine area of interest. Though Delta-T mapping source location can provide complete coverage of a part or
structure, it is best employed as a tool to improve source location around specific areas of expected fracture, which could
be identified via finite element modelling.
Construct map system. A grid is placed on the component over the area of interest and within which AE events will be
located; the higher the resolution of the grid the greater the accuracy. It is possible to increase the resolution of the grid
around features of interest, however it should not be smaller than one wavelength, as this is the minimum location
resolution. It should be noted that sources are located with reference to the grid and not the sensors.
Apply artificial source event to obtain time-of arrival data. Artificial sources [7,8] conducted at the nodes in the grid
provide AE data for each sensor. Several sources at each node are required to provide an average result and to eliminate
erroneous data. It is not essential to have AE data from every node in the grid because missing data points can be
interpolated from surrounding nodes.
Calculate Delta-T Map. From each artificial source, a difference in time of arrival or Delta-T is calculated for each sensor
pair (an array of four sensors has six sensor pairs). The average Delta T for each sensor pair at each node is stored in a
map. These maps can be displayed as contour plots of equal Delta-T.
Compare actual data. To locate an actual AE event, the Delta-T for each pair is calculated. A line or contour on each
map corresponding to the calculated Delta-T can be identified. By overlaying results from each of the sensor pairs, a
convergence point can be identified; the source location. As with time of arrival, a minimum of three sensors is required to
provide a point location and more sensors will improve the location. In theory all lines will intersect at one location,
however, in reality this is not the case. Therefore to estimate the source location, all of the convergence points can be
calculated and a cluster analysis can be conducted on the points to determine the final location.
Once a source is identified a further technique called source location cluster analysis can be employed. The purpose of
source clustering is to automatically identify and associate groups of events, based on a user, controlled spatial criteria
and a number of signals threshold. These clusters can then be ranked based on the total energy or counts contained
within the cluster. As this system is based on spatial and a threshold limits, the Delta-T mapping technique will allow
earlier clustering of signals as it has a greater accuracy in locating events, however as the Delta-T mapping technique is in
development source clustering is not currently possible using this method of source location.
Source Characterisation
Source characterisation currently relies on feature descriptors of recorded waveforms, which use less computer memory
than recording the entire signal. Figure 1 shows the feature descriptors applied to an AE waveform and used thoughout
this work. Currently source identification is performed by examining the relationship between two feature sets, such as
amplitude and counts for clustered sources. However a more appropriate approach would be to automatically examine the
relationship between the two greatest variances in a feature descriptor data set or all feature descriptors.
Principle component analysis (PCA) is a method used to simplify high order data sets to lower dimensions to allow a
simple analysis. PCA is a linear transformation that plots the greatest variance of any data set on one axis whilst the
remaining axis is the second greatest variance allowing any separation to be readily identified. This paper expands on
previous work using PCA of feature data [9,10].
Figure 1. Feature data descriptors for AE waveforms
A further method of data examination would be to use the K-means algorithm. K-means [11] assigns a set of data points
to K-centers, or means. The algorithm is popular due to its fast convergence and simplicity. The algorithm is initiated in a
random state: each data point is assigned as a member of one of K-groups. The set of centers is calculated by taking the
mean of the members of each group, and then each data point is re-assigned to a new group according to its nearest
center. The process is repeated until no points change groups. The algorithm works exceptionally well on well-spaced
groups of Gaussian-distributed data points. In order to make the AE feature data resemble this, the data is normalised,
such as to have unit variance in each feature.
Experimental Procedure
Figure 2 shows a fatigue test specimen, with a variety of holes to interfere with direct wavepaths from source to sensor (a
further investigation was completed using a specimen with changing thickness but is not presented here). The specimen
was made from 3mm mild steel plate. The plate thickness was increased to 9mm at the loading pins by the addition of two
extra plates bolted to either side of the plate, to avoid failure at the loading pins. Six Physical Acoustics Limited (PAL)
resonant sensors were mounted to the plate (as shown in Figure 2), grease was used as an acoustic couplant and the
sensors were held in position with magnetic clamps . The sensitivity of the mounted sensor was evaluated using the H-N
source technique. A 180 x 130 mm grid with a grid density of 10 mm was selected and five H-N sources were conducted
at each available node to form the Delta-T grids. The specimen was fatigued under a load of 3.5 to 35 kN, until failure. All
AE feature data was recorded using a PAL MISTRAS system.
35
0 50.8
40
Load
0 20
Load
40
200
0 20
020
40
0 10
45
AE Sensor
370
5 5 5
Figure 2. Specimen Details (all dimensions in mm)
Results and Discussion
Response of all sensors to the H-N source was above 98dB, demonstrating that all sensors were mounted correctly. A
wave velocity for the first threshold crossing was determined as 4500 ms -1, and used for all TOA source location
calculations. The data from the H-N source events was used to create the Delta-T contour maps, as discussed previously.
It is possible to create similar, theoretical maps for TOA using the above constant wavespeed. Figure 3 displays a
comparison between the Delta-T and TOA source location maps, for one sensor pair. By examining the Delta-T map it is
evident that the wavepaths are interrupted by the holes in the plates which will cause errors in any source location
calculation.
40µs
20µs
0µs
-20µs
Active Sensor Pair
Delta-T
In-active Sensors
Time of Arrival
Figure 3. Comparison of the time difference using Delta T and Time of Arrival source location maps
Figures 4 and 5, show a comparison of the TOA with the Delta-T location techniques of the detected signals. The
specimen geometry and the site of fracture initiation has been superimposed. The plots demonstrate how the Delta-T
technique shows significant improvement. In the TOA results there are four peaks of activity of above 70 events, whist the
Delta-T has only one such peak, furthermore the location of the signals is more compact, demonstrating that clustering of
the source would be achieved earlier using the Delta-T technique. Based on the location of the initiation of fracture and
the closest peak cluster the TOA and Delta-T techniques show errors of 15.5 and 8.0 mm respectively demonstrating a
reduction in error of 48%.
Fracture Initiation
Peak of Events
Y-position [m]
0.12
>70
>35
>20
>9
>4
<=4
0.08
0.04
0
0
0.04
0.08
0.10
0.16
X-position [m]
Figure 4. Source location of detected results (TOA)
Fracture Initiation
Peak of Events
0.12
153
Y-position [m]
125
0.08
93
62
31
0.04
0
0
0
0.04
0.08
0.10
0.16
X-position [m]
Figure 5. Source location of detected results (TOA)
A cluster analysis based on the TOA location is shown in Figure 6, a cluster analysis of the Delta-T location is currently not
possible as the technique is in development. The solid boxes with numbers show the sensor positions and the boxes with
a letter above represent a group of signals that meet the cluster criteria. The cluster analysis was performed with a cluster
size of 0.02 x 0.02 m and a threshold of 50 events. A visual examination of the plot shows that there are to three distinct
groups of signals, one centred around the crack, one around the lower 20 mm hole and a final group below sensor six. To
perform an analysis of the feature data from each of these groups a sample of data was extracted based on the highestranking cluster within each group.
0.36
0.32
Fracture Initiation
0.28
Y-position [m]
0.24
>70
>35
>20
>9
>4
<=4
0.20
0.16
0.12
0.08
0.04
0
0.1
0.2
X-position [m]
0.3
Figure 6. Source Location and Cluster Analysis
0.4
The PCA of the collected signals feature data, irrelevant of source location is shown in Figure 7. Each group has been
assigned a marker and a key is provided at the bottom of the figure. An examination of the plot shows that the three types
of singles have been separated into three regions, however if no prior knowledge of the different groups was known and
the figure was plotted with only one style of marker, then it would be very difficult to separate the groups.
A K-means cluster analysis was performed on the feature data of the collected signals again ignoring source location. An
analysis was performed by defining that there were only three different groups within the data set. Each signal based on
feature data is then assigned to one of the three groups as described previously. The signal was then plotted on to its
relative position on the PCA group to allow a comparison between the two results, as shown in Figure 8. A comparison
between the two sets of results gives a 90.1% correlation.
The two methods of signal analysis have demonstrated their suitability to automatically separating AE fracture signals from
noise in complex geometries. Further work needs to be completed, based on prior knowledge of the feature data of
fracture signals, to be able to automatically identifying crack like groups with high levels of confidence. However the total
confidence of detection will not solely be based on source identification, the tightness of a cluster and location of cluster
(high stress areas) will all provide an increased confidence of fracture detection.
This work has focussed on landing gear modules however, it is believed that any developed source location and
identification methodologies will be able applicable to other structures with complex geometries such as steel box girder
bridges or engine blocks, with a minimal amount of work.
0
-1
-2
-3
-4
0
2
4
Cluster 1
6
Cluster 2
8
Cluster 3
Figure 7. Principal Component Analysis of clustered data
10
0
-1
-2
-3
-4
0
2
4
Cluster 1
6
Cluster 2
8
10
Cluster 3
Figure 8. K-means clustering analysis of clustered data
Conclusions
Delta-T mapping source location provides a novel approach for overcoming particular problems associated with source
location in complex structures with some current location techniques. Previous work using Delta-T has foccuessed on
artificial sources but in this paper it has been shown that the technique can be applied to fracture signals. The error in
location was reduced by approximately half when compared with time of arrival source locaion.
Principal component analysis and K-means clustering provide methods of source separation. Signals from fatigue fracture
were separated from sources of noise and a comparison of results showed a 90.1% agreement. However further work
needs to be completed to able the techniques to identify fatigue fractures automatically.
Acknowledgements
The authors would like to acknowledge the financial support of EPSRC and Messier-Dowty Limited.
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