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Lookup NU author(s): Professor Guofu Yin,
Professor Gui Yun TianORCiD,
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Automatic identification of flaws is very important for ultrasonic nondestructive testing and evaluation of large shaft. A novel automatic defect identification system is presented. Wavelet packet analysis (WPA) was applied to feature extraction of ultrasonic signal, and optimal Support vector machine (SVM) was used to perform the identification task. Meanwhile, comparative study on convergent velocity and classified effect was done among SVM and several improved BP network models. To validate the method, some experiments were performed and the results show that the proposed system has very high identification performance for large shafts and the optimal SVM processes better classification performance and spreading potential than BP manual neural network under small study sample condition.
Author(s): Zhao X, Yin G, Tian G, Yin Y
Publication type: Article
Publication status: Published
Journal: Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument
ISSN (print): 0254-3087
Publisher: Yiqi Yibiao Xuebao Bianjibu