ACOUSTIC
EMISSION & NDT SCIENTIFIC PUBLICATIONS
Advances in Classification of AE Sources
COFREND Conference 2001, CND & Corrosion,
Reims, France, 24-26 April 2001
Dimitrios Kouroussis,
Dr. Athanassios Anastassopoulos, Dr. Jean-Claude Lenain, Dr.
Alain Proust
During the
last few decades, the Acoustic Emission NDT technique has
experienced considerable growth both in terms of sheer
number of inspections and users, and in terms of range of
applications. The capabilities of the technique have been
proven or are under investigation for a vast number of
materials, processes, applications and structures. The
ever-increasing demand for the analysis, characterization
and understanding of the Acoustic Emission (AE) sources,
which are detected during loading of structures, has led
researchers towards the development of methodologies for the
classification of the corresponding AE data. In this
context, Unsupervised Pattern Recognition (UPR) has recently
been applied for the segregation of AE data obtained during
various different applications. The present paper outlines
the basic features of Unsupervised Pattern Recognition for
AE data and reports on some successful application examples
of the technique, with the use of specialized Pattern
Recognition software. It is shown that, upon proper
selection of the UPR algorithms and parameters, the
technique can successfully identify and separate
noise-related AE (EMI, friction, mechanical impacts) from
legitimate AE. Additionally, application of the UPR
technique on AE data obtained during fatigue testing of a
composite wind turbine blade managed to identify the various
co-existing failure mechanisms and to assess their
accumulation on the blade with increasing fatigue cycles up
to final failure. Furthermore, UPR has been applied for the
identification of AE signals arising from hydraulic noise
during loading of an aerial man-lift device. The
corresponding results of UPR were applied by means of
Supervised Pattern Recognition on further AE data obtained
during manipulation of the device’s arm, and hydraulic noise
was very efficiently separated. In overall, it is now
evident that combination of traditional AE analysis
techniques (AE location, AE activity with load etc.) with
UPR analysis can be a powerful tool towards the evaluation
and physical interpretation of AE data. Finally,
future work in the area of the definition of AE signatures
for the existing damage and noise types of Acoustic Emission
in various applications, and application
of SPR could, ultimately, lead to the automation of the
classification and noise elimination procedure and the
establishment of pass/fail criteria for future tests, based
on structurally significant classes.