Abstract
The segmentation of acoustic emission data collected during mechanical tests is one of the current challenges to allow further analysis of damaged materials. Among the existing clustering methods, one of the most widely used is the k-means algorithm. In this paper, a genetic algorithm-based approach is presented. Data sets derived from experimental AE data are processed to highlight the contributions of the new algorithm. Its superiority over the k-means algorithm is demonstrated for several data sets, and especially when a cluster is significantly smaller than the others, or very far and thus behaves as a group of outliers or if the clusters have very different sizes. This method allows the better clustering of AE data even on complex data sets.
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Sibil, A., Godin, N., R’Mili, M. et al. Optimization of Acoustic Emission Data Clustering by a Genetic Algorithm Method. J Nondestruct Eval 31, 169–180 (2012). https://doi.org/10.1007/s10921-012-0132-1
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DOI: https://doi.org/10.1007/s10921-012-0132-1