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

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 consider the influence of dispersion and attenuation effects during propagation of Lamb-waves on the extracted acoustic emission features. This is done by investigating the influence of source-sensor distance by test sources like pencil lead breaks and piezoelectric pulsers. Using the model based algorithm it is possible to calculate the uncertainty of the pattern recognition results as a function of source-sensor distance. It is found that dispersion effects of Lamb-waves do not seriously affect the distinction between microscopic failure mechanisms for source-sensor distances up to 375 mm. We demonstrate that the spatial distribution of acoustic emission sources has a larger impact on the uncertainty of assignment than the absolute source-sensor distance. Applying the proposed algorithm to the current experimental setup, we obtain an uncertainty of classification below 7 % for source-sensor distances below 375 mm. Attenuation is quantified to be 0.165 dB/mm for the A ₀-mode and 0.047 dB/mm for the S ₀-mode. Within the source-sensor distance of 375 mm this causes severe attenuation of the signal amplitude and thus prohibits detection of weak acoustic emission signals long before the uncertainty of the classification method reaches 10 %.