Pattern recognition approach to identify natural clusters of acoustic emission signals

Abstract

A new approach is introduced to identify natural clusters of acoustic emission signals. The presented technique is based on an exhaustive screening taking into account all combinations of signal features extracted from the recorded acoustic emission signals. For each possible combination of signal features an investigation of the classification performance of the k-means algorithm is evaluated ranging from two to ten classes. The numerical degree of cluster separation of each partition is calculated utilizing the Davies-Bouldin and Tou indices, Rousseeuw's silhouette validation method and Hubert's Gamma statistics. The individual rating of each cluster validation technique is cumulated based on a voting scheme and is evaluated for the number of clusters with best performance. This is defined as the best partitioning for the given signal feature combination. As a second step the numerical ranking of all these partitions is evaluated for the globally optimal partition in a second voting scheme using the cluster validation methods results. This methodology can be used as an automated evaluation of the number of natural clusters and their partitions without previous knowledge about the cluster structure of acoustic emission signals. The suitability of the current approach was evaluated using artificial datasets with defined degree of separation. In addition the application of the approach to clustering of acoustic emission signals is demonstrated for signals obtained from failure during loading of carbon fiber reinforced plastic specimens.