A real-time approach to acoustic emission clustering

Evolution of Mechanical Twinning during Cyclic Deformation of Mg-Zn-Ca Alloys

“…The AE signal processing has experienced revolutionary changes with the advent of computer and information technologies in recent years. A novel statistical method of AE signal categorization and recognition developed in [25] has been proven effective for real-time analysis of elementary deformation mechanisms in advanced materials including Mg alloys. The method was successfully tested and applied for discrimination between contributing deformation mechanisms such as dislocation slip, twinning and martensitic transformations from the AE signal at various stages of deformation in several materials [26][27][28][29][30].…”

Acoustic Emission Assessment of Impending Fracture in a Cyclically Loading Structural Steel

“…Compared with signal classification methods based on single features detected via threshold-based acquisition, pattern recognition techniques are computationally intense. However, they are significantly more adequate for processes investigated and, fortunately, can now be implemented in a variety of efficient and elegant mathematical ways [14]. (4) The most important finding, which was observed in all tests at both temperatures, is that, as final rupture approached, the AE energy per cycle increased, while the spectral density function progressively shifted to the low-frequency domain, as reflected in the median frequency reduction.…”

“…As has been suggested in [14] and then probed in several studies on different materials [18][19][20], satisfactory results of clustering are obtained if the if the Kullback-Leibler divergence (KL) in its symmetric version is used as the distance measure ||•|| 2 as…”

“…These functions are used as input for the recently proposed adaptive sequential k-means clustering (ASK) algorithm [14]. In the standard k-means procedure, each cluster is represented by a set of variables with the middle point of the cluster-centroid.…”

A Preliminary Study Application Clustering System in Acoustic Emission Monitoring

“…A major challenge of AE analysis is how to discriminate the AE signals due to the different damage mechanisms [10][11][12][13] Generally accepted ways of discriminating AE signals is cluster analysis. Cluster analysis can be define as generic name for variety of mathematical method, classification numbering in hundreds and that can be used to find out the similarity [14]. The three important things in cluster analysis which are, the selection of AE parameters will be used, choosing the clustering algorithm and validation of the define cluster [15][16][17].…”