Evaluation of echo features of ultrasonic flaws and its intelligent pattern recognition

In this paper, three types of weld flaw were taken as target, evaluation and recognition of flaw echo features were studied. On the basis of experimental study and theoretical analysis, 26 features have been extracted from each echo samples.A method which is based on the statistical hypothesis testing and used for feature evaluation and optimum subset selection was explored Thus. the dimensionality reduction of feature space was brought out, and simultaneously, the amount of calculation was decreased. An intelligent pattern classifier with B-P type neural network was constructed which was characterized by high speed and accuracy for learning. Using a half of total samples as training set and others as testing set, the learning efficiency and the classification ability of network model were studied. The results of experiment showed that the learning rate of different training samples was about 100％. The results of recognition was satisfactory when the optimum feature subset was taken as the sample's feat

Evaluation of echo features of ultrasonic flaws and its intelligent pattern recognition

In this paper, three types of weld flaw were taken as target, evaluation and recognition of flaw echo features were studied. On the basis of experimental study and theoretical analysis, 26 features have been extracted from each echo samples. A method which is based on the xtatislical hypothesis testing and used for feature evaluation and optimum subset selection was explored. Thus, the dimensionality reduction of feature space was brought out, and simultaneously the amount of calculation was decreased. An intelligent pattern classifier with B-P type neural network was constructed which was characterized by high speed and accuracy for learning. Using a half of total samples as training set and others as testing set, the learning efficiency and the classification ability of network model were studied. The results of experiment showed that the learning rate of different training samples was about 100%. The results of recognition was satisfactory when the optimum feature subset was taken as the sample's feature vectors. The average recognition rate of three type flaws was about 87.6%, and the best recognition rate amounted to 97%.