Quantification of the Uncertainty of Pattern Recognition Approaches Applied to Acoustic Emission Signals

摘要

Acoustic emission analysis is a nondestructive technique frequently used to assess the integrity of fiber reinforced plastics. Pattern recognition techniques have shown great potential to identify microscopic failure mechanisms in plate-like structures. Because every assignment of an acoustic emission signal to a respective failure mechanism is possibly associated with an error, one key question is the reliability of the assignment method. It is useful to distinguish between the uncertainty of the assignment and the false assignment of an acoustic emission signal to a group of signals. The first is owed to statistical effects and the reliability of the classification method itself. The second is caused by false conclusions or disputable assumptions on the source mechanisms. The present study will focus on the first aspect. For this purpose, we propose a model based algorithm that estimates the uncertainty of a feature based pattern recognition approach based on cluster validity indices. Further, we demonstrate the application of the algorithm to experimental acoustic emission data obtained from a double cantilever beam specimens with unidirectional layup of carbon fiber reinforced polymer. Based on previous investigation we use a pattern recognition approach to distinguish between different failure mechanisms like matrix cracking, interfacial failure and fiber breakage based on the frequency features of the acoustic emission signals. We