Rule extraction for classification of acoustic emission signals using Ant Colony Optimisation

Authors:
S. N. Omkar;Raghavendra Karanth U
Affiliations:
Department of Aerospace Engineering, Indian Institute of Science, Bangalore 560012, India;Department of Computer Science, P.E.S. Institute of Technology, Bangalore 560085, India
Venue:
Engineering Applications of Artificial Intelligence
Year:
2008

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Abstract

Ant Colony Optimization (ACO) is used to obtain rules that can classify the data into pre-defined classes. It can be used to classify acoustic emission (AE) signals to their respective sources. ACO based technique has an advantage over conventional statistical techniques like maximum likelihood estimate, nearest neighbor classifier, etc., because they are distribution free, i.e., no knowledge is required about the distribution of data. AE test 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. Classification of AE signal is done using Ant Colony Optimization, and the simplicity of the rules generated is emphasized.