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Study on flaw identification of ultrasonic signal for large shafts based on optimal support vector machine

Lookup NU author(s): Professor Guofu Yin, Professor Gui Yun TianORCiD, Ying Yin

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Abstract

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.

Publication metadata

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

Year: 2008

Volume: 29

Issue: 5

Pages: 908-913

ISSN (print): 0254-3087

ISSN (electronic):

Publisher: Yiqi Yibiao Xuebao Bianjibu

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