An unsupervised pattern recognition approach for AE data originating from fatigue tests on polymercomposite materials
An approach for clustering massive acoustic emission data issued from fatigue test. The method embeds a fast sequential AE feature selection and noise cancellation. AE sources are identified using the GustafsonKessel clustering algorithm. The number of clusters is adapted during fatigue to cope with new AE sources. The evolutions of clusters during loading are related to damages kinetics.
organicmatrix composites acoustic emission clustering fatigue datasets noise reduction sequential feature selection