WAVELET PACKET DECOMPOSITION FOR THE IDENTIFICATION OF CORROSION TYPE FROM ACOUSTIC EMISSION SIGNALS
Corrosion causes a degradation of the structural integrity of petrochemical plants, nuclear power plants, ships, bridges and other constructions containing steel with the consequence that people and the environment may be exposed to dangerous situations. The detection of corrosion and the prediction of the type of corrosion are 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 highest 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 using the most discriminative wavelet coefficients are compared for the prediction of three types of corrosion and the absence of corrosion.
wavelets information theory pattern recognition