Title: Wavelet packet decomposition for the identification of corrosion type from acoustic emission signals
Authors: Van Dijck, Gert ×
Wevers, Martine
Van Hulle, Marc #
Issue Date: Jul-2009
Publisher: World Scientific
Series Title: International Journal of Wavelets, Multiresolution and Information Processing vol:7 issue:4 pages:513-534
Abstract: Corrosion causes a degradation of the structural integrity of petrochemical plants, nu-
clear power plants, ships, bridges and other constructions containing steel with the con-
sequence that people and the environment may be exposed to dangerous situations. The
detection of corrosion and the prediction of the type of corrosion is studied in this article
by means of the acoustic emission technique. We use a wavelet packet decomposition to
compute features from the acoustic emission signals. The basis functions with the high-
est discriminative power are selected according to the highest pair-wise Kullback-Leibler
divergence between distributions of wavelet coefficients. It is proven that the pair-wise
Kullback-Leibler divergence used in the local discriminant basis algorithm requires class
conditional independence of the wavelet coefficients. Several classification algorithms us-
ing the most discriminative wavelet coefficients are compared for the prediction of three
types of corrosion and the absence of corrosion.
ISSN: 0219-6913
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Laboratory for Neuro- and Psychofysiology
Mechanical Metallurgy Section (-)
Research Group Neurophysiology
× corresponding author
# (joint) last author

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