Abstract
A new approach has been suggested to revealing acoustic-emission data-flow discords that are related to the formation and growth of flaws. The discords were discovered using a statistical criterion and the Caterpillar SSA principal components method. The approach has proved its consistency when tested on realistic acoustic-emission data-flow models.
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