Current practice and applications of acoustic emission
Prof K M Holford, Dr R Pullin and Mr M Baxter
School of Engineering, Cardiff University
Queen’s Buildings, The Parade, Cardiff, CF24 3AA
[email protected], [email protected]
ABSTRACT
Acoustic emission (AE) is a technique that is often misunderstood and misrepresented, but is fast emerging as a powerful
method in terms of damage assessment and structural health monitoring. Early pioneers of the technique during the late
1960’s through to the early 1980’s such as Dunegan, Pollock, Wadley, Scruby, Birchon, Schofield, Beattie, Proctor, Harris, and
Ono produced outstanding scientific analyses despite the limitation of their equipment and indeed many of today’s researchers
are revisiting this work. AE is currently experiencing an increase in popularity due to recent advances in high-speed digital
waveform-based AE instrumentation which permits vast numbers of AE waveform signals to be digitised and stored for
analysis. Coupled with improvements in high fidelity, high sensitivity broadband sensors and the development of advanced
PC-based signal analysis software, these advances have given rise to recent work that has been directed at an enhanced
understanding of AE signal propagation in terms of guided acoustic modes. The approach, more recently designated “Modal
Acoustic Emission”, offers the potential to depart from the traditional reliance on statistical analysis and significantly improve
the structural monitoring capabilities of AE.
Wave analysis has been well-established for many decades; in fact Lamb wave analysis has been exploited by researchers in
ultrasonic techniques for over thirty years. Researchers in AE testing, however, have largely ignored applications of Lamb’s
theory to analysing AE data –probably due to the fact that early AE experiments were conducted on small specimens, where
the theory is not applicable. A further reason is that ultrasonic researchers transmit a harmonic wave to select the mode
desired for a given plate thickness, while AE researchers work with waves generated by a transient event and therefore such
selection is not possible
AE differs from other methods of investigating material deformation and damage processes in three significant respects.
Firstly, the energy that is detected originates from the specimen itself, rather than being supplied from an external source.
Secondly, it does not take a ‘snap shot’ of the condition of a specimen, but instead detects the actual dynamic processes
associated with the degradation of structural integrity, and thirdly, a sensor located anywhere in the vicinity of an AE source
can both detect and locate the resulting emission. The result is a truly powerful monitoring technique that has considerable
potential in a variety of applications.
Source characterisation is one of the most challenging areas of AE research, because the signal at the sensor bears very little
resemblance to the dynamics of the source event. There are two main approaches to the solution of this problem. The
deterministic approach attempts to develop quantitative relationships between source parameters and physical measurements
of the transducer signal. Alternatively a stochastic approach can be utilised; this uses distribution, rate and correlation analysis
of AE feature data from a range of different defect sources in samples of interest to compile empirical correlations with
measured source properties and behaviour. This pattern recognition approach can use neural networks and visualisation and
clustering techniques. Furthermore, a combination of both the stochastic and deterministic can be a very powerful and
compelling technique for many applications.
AE WAVE PROPAGATION
Modern AE analysis, often concerned with waveform analysis, require a deep understanding of the way in which AE waves
propagate. Rindorf [1], Pollock [2], Miller [3], Gorman [4], Carter [5] and Pullin [6] all give detailed accounts of the complex
problem of wave propagation; the major points are summarised below. Many modern applications are concerned with
structural elements that are plate-like; therefore the focus has been placed on classical plate wave propagation.
• Initially waves propagate from the source as bulk waves. These elastic waves propagate in two basic forms, longitudinal
(pressure) and transverse (shear). When boundary conditions are introduced such as a surface, a further wave mode may
exist, a surface wave or Rayleigh wave (Figure 1).
Figure 1. Longitudinal, transverse and surface waves modes in a solid [1]
• In a plate, where two surfaces are sufficiently close together, there are many reflections and mode conversions. The waves
couple together and form more complex surface waves known as Lamb waves (Figure 2). The two major modes are the
symmetric or extensional (S0) and the asymmetric or flexural (A0). Geometric conditions allow for higher order waves, such
as S1 and A1, however they tend to have lower amplitudes, contain little energy, and are often difficult to identify in the ringdown of the transducer. Lamb wave behaviour is complex and dependent on plate thickness and frequency content.
Frequency components of each mode travel at different wave velocities depending on the thickness of the plate. Dispersion
curves, based on Lamb’s homogenous equation, are used to describe the relationship between wave velocity of each mode
and the product of plate thickness and frequency. These are readily available for most materials and indicate the “triple
point”, which identifies the frequency for a particular thickness of plate at which the S0, A0 and A1 modes travel at the same
velocity; most energy is contained at this frequency. For steel, the triple point is at 2mm.MHz and a wave velocity of
≈3300ms-1. For a 10mm steel plate the triple point, and therefore the peak energy, exists at 200kHz.
Figure 2. Lamb wave propagation in a plate [1]
• Most AE applications lie within the 20kHz – 500kHz frequency range. It is commonly understood that the extensional mode
has a higher group velocity than the flexural mode. The extensional mode is of lower amplitude and occurs as a precursor
at the beginning of the waveform and the flexural mode is typically of higher amplitude and carries the peak of the signal
(Figure 3).
• Interaction of surface or plate waves with any form of boundary, such as thickness change or geometric feature, will cause
both reflections and mode conversions making wave propagation models in complex geometric structures problematic.
Identification of the triple point is then not possible.
Figure 3. AE waveform showing arrival of the two primary wave modes
SOURCE CHARACTERISATION
In order for the acoustic emission technique to be used as an on-line, real-time NDT technique one needs to be able to identify
the failure modes and the AE signals that arise from these, as well as all the other sources of AE for that particular application.
If all AE signals could be unambiguously characterised according to their generation (or source) mechanism then the problem
would be solved, however this is rarely the case. In practical applications, for instance in fatigue monitoring, many 1000’s of
signals are recorded at each sensor every second, but only a very small number of the signals result from fatigue-related
processes. Each signal received exhibits its own set of signal characteristics and theoretically a unique set of characteristics
should be produced by a certain source in a given material and geometry, however, there are many, many factors that
influence the signal as received by the AE processor and it is this aspect that has fascinated and perplexed researchers for
many years.
The recorded AE signal from a discrete or burst type emission can be summarised by a number of basic parameters; peak
signal amplitude, rise-time, average frequency, ring-down count and energy. Most of these are complex functions of the
frequency response of the sensor and structure, damping characteristics of the sensor and propagation medium, coupling
efficiency, sensor sensitivity, amplifier gain and threshold voltage. In the past, due to storage limitations, AE systems were
only able to record these parameters, known as AE feature data, and not whole waveforms throughout a test, so analysis
techniques were limited. For this reason, commercial systems have largely been developed to pre-process AE information
and store mainly waveform parameters, which do provide a good indication of the intensity or severity of any AE source; this
information can be used to determine whether the structure under test is accumulating damage. Modern microprocessors have
enormous storage capacity and fast processing power, which has opened up the possibility of extensive waveform analysis
which should pave the way to a true understanding of wave propagation and source characterisation.
There have been many attempts to relate some function of AE to the fracture mechanics of a fatigue crack source, with
conflicting results only partially explained by differences in experimental technique. Many have examined the correlation
between AE feature data and fracture mechanics parameters in an attempt to provide some measure of damage, a
comprehensive review of which is presented by Muravin et al [7]. Notable work in the field includes that of that of Gerberich &
Hartbower [8], Dunegan et al [9], Radon & Pollock [10], Harris & Dunegan [11], Lindley et al [12], Williams et al [13], Baram
[14] and Berkovits & Fang [15]. The conflict lies in the fact that, in order to obtain meaningful relationships with fracture
mechanics parameters, only the AE generated by the primary sources related to that fracture mechanics parameter should be
used, and the separation of these sources from all others is a complex task. However, the most significant limitation of the
fracture mechanics correlation approach is that experimental correlations are highly specific to a particular material, specimen
geometry, loading regime and instrumentation system and are only valid for the precise conditions in which they were
obtained.
A deterministic approach, using wave propagation analysis can be seen for example in the work of Gorman and Prosser [16],
Prosser et al [17], Majii and Satpathi [18] and Dunegan [19]. Recent efforts by several teams have concentrated on the
prediction of received waveforms; and excellent example of this can be seen for example in the work of Wilcox et al. [20]. A
stochastic approach can also be utilised. This uses distribution, rate and correlation analysis of AE feature data from a range
of different defect sources in samples of interest to compile empirical correlations with measured source properties and
behaviour; this pattern recognition approach can use neural networks and visualisation and clustering techniques is has been
explored by a number of researchers including Holford et al [21], Manson et al [22] Rippengill et al [23], Roy et al [24] and Ono
et al. [25]. Clearly, the most attractive progression is the automatic classification of acoustic emission signals into classes
representing their generation mechanism. Automatic classification of the signals offers the ability for the technique to be used
as a means of assessing and monitoring structures for fatigue damage; the most common form of failure within structural
materials. Previous work exists in the literature extensively utilising and documenting pattern recognition procedures, and
substantial information is available concerning acoustic emissions and fatigue damage, e.g. [26,27].
SOURCE LOCATION
The single most attractive feature of AE monitoring remains the ability to locate the source of the emission. The most widely
used method of location is ‘Time of Arrival’ (TOA) source location and this is the basis used in the commercial AE software.
The method is explained in Miller [3] and Rindorf [1]. More recent work has concentrated on improving the accuracy of this
technique by alleviating the problems inherent in selection of a single wave velocity as outlined by Holford [28].
Single sensor source location shows enormous potential; this method is based on the dispersive nature of Lamb waves and is
therefore only applicable in plates and at a distance where plate waves have developed. By determining the arrival times of
particular mode components, the distance to the source can be computed through temporal separation. If the wave is detected
in an appropriate manner by a suitable broadband sensor, separation of the different mode components can be observed.
Pullin [6] offers a comprehensive review of studies into Lamb wave location. The two predominant wave modes travel at
different velocities (figure 4) therefore an estimation of source distance can be made.
Figure 4. Single Sensor Modal Analysis Location (SSMAL) [29]
Work by Maji [18], Ziola [30] and Dunegan [31] demonstrates the location of HN sources using simple single sensor source
location, whilst work by Holford and Carter [32] examines the use of Lamb waves for location of H-N sources in a long beam.
Recent work by Cole and Carlos [33] presents results from the pressurisation of a slug catcher. A cluster of events was
located using both TOA and SSMAL techniques. The source was not concentrated, however the location by both methods was
comparable.
A new approach has been explored by Nivesrangsan et al.[34] present a location method based on energy attenuation. The
methodology follows TOA source location, replacing time difference with energy difference and wave speed, CAE, with an
attenuation coefficient, 1/k. The authors report improved source location against the TOA method in detecting multiple-source
signals in a diesel engine. However, as this method is based on the same logic as TOA source location, similar error sources
apply; including weak AE sources, inaccurate sensor location, calculation of energy attenuation coefficients, energy
calculations, dispersion of energy and human error.
Computerised tomography (CT) was developed for medical use (the CT scan) in the early 70’s by Hounsfield and Cormack, as
described by. Kak et al. [34]. Based on this concept, Schubert [36] presented the basic principles of “AE tomography” using
Algebraic Reconstruction Techniques (ART). In this method, geometric features, such as holes, will assume an altered wave
speed to take account for the change in wavepath. Results indicate an improvement in location from ±4.54mm to ±0.9mm for
enhanced localisation and AE tomography respectively. The author states that this method is able to not only improve source
location accuracy, but also provide AE imaging of the assessed area. Schubert [37] presents results from aluminium plate with
a saw cut. The array is unable to detect the cut until it intersects at least one of the wave paths between sensors; this is clearly
a disadvantage of this method.
A recent development in location has arisen from the need to provide a robust, accurate source location technique in complex
geometries with multiple propagation paths. The technique has been termed “Delta T mapping” and is reported by Baxter et al
[38].
MODAL ANALYSIS
Analysis of the relationship between extensional and flexural mode has led to a method for identifying the orientation of a
source event, this shows enormous potential for many industrial applications. Gorman and Prosser [16] simulated AE signals
in an aluminium plate at angles of 0°, 30°, 60° and 90° with respect to the plane of the plate using the H-N source. Analysis of
the two modes revealed a relationship between amplitude and the source orientation angle. H-N sources were further used by
Carter [5] during a study of a steel I beam. Analysis of the measured amplitude ratio (MAR), of the resultant waveform allowed
the orientation of the source to be identified. Carter further examines the relationship between the MAR and distance and
depth of source. It should be noted that studies have largely been confined to artificial sources.
INDUSTRIAL APPLICATIONS
The AE technique has been successfully applied in a range of industries, for example civil, aerospace, and offshore.
The major application of AE to concrete structural elements began in the 1970’s with the work of Ono and Ohstsu [39, 40] and
they have remained prolific workers. Shah and Li have studied source location and orientation [41] but more recently the
multiscale phenomenom of cracking has led researchers such as Carpentieri to consider fractal theories [42] to interpret AE
data. Grosse [43] has used moment tensor analysis for high resolution damage analysis of fracture mechanics specimens.
In recent practical field trials, Shiotani [44] utilised AE to evaluate the repair of deteriorated concrete piers of intake dams. AE
activity was monitored along with water pressurisation tests, results showed that AE activity showed good agreement with
actual damage condition. A further application of AE is to monitor corrosion on industrial plant and structures, Cole and
Watson [45] reports on successful investigations of monitoring carbon steel storage tanks, the corrosion of reinforcing bar
encased in concrete and the corrosion of process equipment, predominantly pipe work.
A major current application is the monitoring of steel reinforcement concrete hinge joints. Hinged joints are not easily
accessible for inspection or maintenance due to their form and their location over or under live traffic lanes. They are
vulnerable to deterioration in the event of bridge deck waterproofing failure, which can cause steel reinforcement bar
corrosion. This reinforcement is crucial to the integrity of the joint, and the loss of reinforcement section can induce higher
stresses leading to eventual failure by yielding. A number of investigations have been completed on hinge joints, one using
scale model hinge joints statically loaded [46] and two field investigations [47, 48]. Figure 5 shows the attachment of AE
sensors to the soffit of a hinge joint structure. The structure was monitored under normal loading conditions and the resulting
planar location plot is also shown in Figure 5. Distinct bands of vertical emission can be seen and coincide with the position of
the internal reinforcement hinge joint bars. These bands occur at larger intervals than the 1ft distance that the construction
drawings show and implies that the AE is detecting sources at some bars but not every bar. It is believed that the detected
activity is due to regions of concrete micro-cracking around the reinforcing steel.
The application of AE to steel structures started as early as 1972 when Pollock and Smith [49] collected data during proof
loading of a military transportable bridge. Since then numerous investigations have been completed which are summarised by
Y-position (m)
Pullin [6]. The techniques developed during these investigations have since been applied to monitor weld joints in off-shore
structures.
Hinge
X-position (m)
Figure 5. Photograph of installed AE sensors on a bridge soffit and location results from three days monitoring
Aerospace engineering is promising area for AE testing. Lee et. al. [50] reported on the monitoring of fracture growths in large
aluminium alloy and comparing detected signals with a two-dimensional finite model. The aim of the work and further work by
the authors [51, 20] is to be able to characterise source signals in aerospace components by using a regressive wave
propagation model.
In real components, however there is limited published work due to commercial sensitivity, however one published
investigation reports on an AE investigation of an aluminium landing gear component undergoing testing to investigate its
fracture resilience [52]. The AE investigation was implemented after 83,000 flight cycles had been completed and NDT at this
point had shown that the component contained no damage. Figure 6 shows a planar location plot as a result of 2,000 cycles of
monitoring. It can be seen that there is a clustered group of activity around the grease pin. Fretting damage at this location
was confirmed using dye penetrant testing (Figure 6).
Figure 6. Planar location plot showing located areas of damage and results of a dye penetrant test confirming the presence of
damage
CONCLUSIONS
The role of acoustic emission testing in non-destructive testing is ever increasing, largely due to recent advances in highspeed digital waveform-based AE instrumentation which permits vast numbers of AE waveform signals to be digitised and
stored for analysis. As a result of these advances, there are an increased number of techniques for source analysis and
characterisation, indeed using a single waveform it is now possible, in plate structures, to identify source to sensor position
and source orientation.
AE is also widely exploited commercially and the technique is now at a sufficiently advanced stage to allow damage
assessment and NDT to be conducted with an increasing degree of confidence.
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