Identification of multilayered structure properties using wavelet-fractal dimension of ultrasonic data

Abstract

This paper present a wavelet-fractal dimension approach for extracting feature from ultrasonic non-destructive evaluation (NDE) data of a test multilayer adhesive structure specimen. These features are then used to estimate the specimen's bonded quality. When the multilayer adhesive structure undergo ultrasonic NDE, echo from interior layers often overlap severely, causing information from individual layers to be obscured or lost completely. A new idea, using wavelet as the ruler to define fractal dimension, is proposed in this paper, based on self similarity of knowing essence of things from collectivity to part, from macrocosm to microcosm, in fractal theory and wavelet transform. Wavelet-fractal dimension is defined as the sum of high-frequency values of decomposed signals. The fractal characteristics of ultrasonic data are gotten from this dimension. The extracted features displayed are effective to analyze and identify the bonded properties. It is believed that the features extracted via the method outlined in this paper provide considerable promise in the area of the test of multilayered structure.

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“…Wavelet and fractal have been proven to be excellent methods for processing this kind of signals. In [ 10 ], the wavelet transform is used to analyze lamb-wave ultrasonic NDE signals; in [ 11 ], the fractal property of time series serves as a preprocessing tool for the classification of defects probed by ultrasonic signals; in [ 12 ], the wavelet transform and fractal theory are combined to analyze the ultrasonic NDE data on the multi-layer adhesive structure. In the following, wavelet packet and fractal analysis were used for feature extraction of echo signals.…”