Wood acoustic emission signal classification based on IMF's features

Abstract

Nondestructive testing technology of wood acoustic emission(AE) signal is of great significance to evaluate wood internal damage. In order to achieve more accurate and adaptive evaluation, we propose an AE signal analysis method combining instantaneous frequency and power to extract the signal features of different the Intrinsic Mode Function(IMF) components. Then input the SVM classifier for classification and recognition, and adopt the Receiver Operating Characteristic (ROC) curve as the evaluation index to evaluate the classification model of different IMF components. The results show that the instantaneous frequency and power can clearly display AE signal features. The IMF components decomposed by EMD are classified by extracting features, and the classification accuracy of IMF 1 component up to 88% is the highest one. It indicates that IMF 1 component contains a large number of effective AE signal features, which can be utilized for the identification of wood damage and fracture state.