Artificial Bee Colony for Classification of Acoustic Emission Signal Source

Abstract

In this paper the approach for classification of acoustic emission signals to their respective sources is employed using a swarm intelligence technique called artificial bee colony. In this work, artificial bee colony is employed to train a multilayer perceptron neural network which is used for the classification of the acoustic emission signal to their respective source. Acoustic emission is carried out using pulse, pencil and spark signal source on the surface of solid steel block. The signal parameters are measured using AET 5000 system. To begin with, the complexity for acoustic emission data set is verified using conventional statistical technique like principal component analysis and traditional training algorithm like multilayer perceptron neural network trained using the backpropagation algorithm. The experiment shows in both the case the classification is not accurate. For this complex acoustic emission data set multilayer perceptron neural network trained using the artificial bee colony algorith...