Investigations on Numerical Techniques for Detecting Variations in Acoustic Emissions

The objective of this paper is to present a hybrid methodology of analysing acoustic signals arising in industrial processes through comparisons of known numerical techniques such as clustering. Apart from data acquisition and pre-processing, the other essential component of using acoustics is to design an analysis methodology, that can culminate in practical applications. This paper applies Gaussian Mixture Models and Self-Organising Maps to cluster pre-processed AE hits obtained from acoustic sensors in the form of tensile, shear and mixed modes of compression on a material. For an in-depth analysis, custom features such as high peak regions, low peak regions, strongly hit and weakly hit signals are introduced to compare with the clusters formed. The results show that for small AE signals that are obtained or extracted after events detection, a time-domain based clustering can be applied and used for isolating similarities and distinctions among the signals belonging to the same group.