Classification of acoustic emission signals via Hebbian feature extraction

Abstract

The automatic classification of acoustic emission signals is discussed. A multilayer heterogeneous network was designed to improve the performance of the structure and reduce training time. The network consists of two basic subnetworks, a compression subnetwork with a generalized Hebbian algorithm, and a classification subnetwork with a backpropagation algorithm. Feature extraction and data compression are accomplished by the compression network first, and then classification is done by the classification network using the compressed data. The signal preprocessing provided by the Hebbian algorithm contributes to the optimal linear data reconstruction with maximal variance and minimal error. Classification performances using both compressed and raw data are compared. Significant reductions in the size of the network and in the training time were achieved in a simulation. The network structure, generalization capability, and classification accuracy are all discussed. >