Application of Wavelet Analysis and Artificial Neural Network Pattern R ecognition to Flaw Classification in Ultrasonic Testing
Haiyan Zhang · Journal of China University of Mining and Technology · 2000
?According to the nonstationarity of pulse echo signals of flaw in ultrasonic testing, a method of flaw classification based on the com bination of wavelet transform with pattern recognition was presented . Method of extracting characteristic values reflecting the flaw properties usin g wavelet transform and the method of qualitatively recognizing the characterist ic values using pattern recognition were studied. An experimental system was used to test the method abov e, by which some real weld flaws were processed. Firstly the feature values of f laws were extracted with wavelet transform, then the flaws were classified with back propagation neural networks. The problems of signal data acquisition a n d the eliminating of pulse interference signals brought out from data acquisitio n were also considered carefully during the experiment. The results show that by this method human effects on qualitative recognition of flaws can be reduced to some extent, and high accuracy of flaw classification can be obtained.