Acoustic emission signals classification based on support vector machine

The study concerns with classification of acoustic emission signals in composite laminates using support vector machine (SVM). Wavelet packet analysis is performed initially to extract the features and to reduce the dimensionality of original data features. The SVM classifiers are trained with a subset of the experimental data for known fault conditions and are tested using the remaining set of data. The result shows that muti-class SVM produces promising results and has potential for use in AE signal classification.