ARTIFICIAL NEURAL NETWORK AND WAVELET TRANSFORM OF FLAW ECHO LOCATIVE

摘要

This paper presents our research of locating an ultrasonic NDT flaw echo (determining the depth of flaw) of fiber reinforced composites using wavelet transforms and an artificial neural network. The depth information is extracted from a complex envelope of wavelet coefficients and two neural network methods - segmental mapping and continuous mapping are applied to locate the flaw echo. Wavelet transforms can provide joint time - frequency distribution with the multi resolution structure. In this paper, the orthonormal bases of compactly supported wavelets are constructed and the algorithm of fully sampling discrete wavelet transform, which is capable of extraction more information than conventional diadic sampling discrete wavelet transform, are established. Our ultrasonic NDT signals of fiber reinforced composites are processed by the fully sampling discrete wavelet transform. The results show that though the original flaw echo is not always significant in amplitude, the wavelet transform of flaw echo usually has larger wave amplitude - at least in one scale. So we use complex amplitude method to extract the position(depth) information of flaw echo. As the flaw echo complements are most significant in scale 4 and 5, we join the complex amplitude of the two scales into one dimensional signal to input the artificial neural network. As to the artificial neural network, we employ the model of multilayer perception and use the back propagation(BP) algorithm to train it. We propose two methods to locate the flaw echo - segmental mapping and continuous mapping. In the segmental mapping, 3 neural nodes are set in the output layer to map the position of flaw echo to 8 segments and the correction rate reach 85.5%. While in the continuous mapping, only one neural node is set in the output layer to map the position of flaw echo to the corresponding continuous output value and the average prediction error is 5.1%.