In order to improve the ability of flaws identification in ultrasonic testing, a flaw-recognition model based on Probabilistic Support Vector Machine, combined with Empirical Mode Decomposition and Dempster-Shafer Evidence Theory, was proposed to test on a large rotor with multi-ultrasonic sensors. Firstly, the characters of test signal were extracted with theory of Empirical Mode Decomposition. Secondly, a step forward was added to the output of the SVM classifiers to choose the category with a maximal posteriori probability, thus, an algorithm model of Probabilistic Support Vector Machine was presented. The outputs of Probabilistic Support Vector Machine were just the support degree of ultrasonic flaws. Lastly, the results of ultrasonic defects recognition were obtained with Dempster-Shafer evidence theory. Results showed that the proposed model in this paper overcame the limitation that the outputs of the traditional support vector machines were un-calibrated and should not be used to determine the category when a multi-class problem was presented. Comparison demonstrated that this model had a better performance in improving the recognition accuracy and nicety ratio of defects identification than the model of NN combined with DS and the model of SVM with odd sensor.
