Monitoring of structural integrity using unsupervised data clustering techniques

Abstract

This work presents a comparative study of three unsupervised data clustering techniques used to perform the monitoring of the structural integrity of an agricultural tractor. The techniques used in this study are: K-Means, Fuzzy C-Means and Kohonen artificial neural network. These techniques are intelligent learning tools, which provide a classification of the information based on the similarity clustering. The main application of these tools is to assist in structures inspection process in order to identify and characterize flaws as well as assist in making decisions, avoiding accidents. To evaluate these algorithms the modeling was performed and signs of simulation from a numerical model of an agricultural tractor. The results obtained by the methodologies presented a comparative study.