The Ultrasonic Signal Identification of the Nickel-Based Superalloy Based on the Wavelet Neural Network

Abstract:

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By using the good time-frequency localized nature of the wavelet transformation and self-learning function of the traditional artificial neural network, this paper constructed a wavelet neural network model for the blemish signals in ultrasonic testing of the nickel-based superalloy GH4169, and it could recognize types of the blemish signals. The results show that the method is effective in fault diagnosis. Finally the article has confirmed its feasibility and superiority.

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Periodical:

Edited by:

Yi-Min Deng, Aibing Yu, Weihua Li and Di Zheng

Pages:

1581-1584

DOI:

10.4028/www.scientific.net/AMM.37-38.1581

Citation:

X. Yin and Y. P. Liu, "The Ultrasonic Signal Identification of the Nickel-Based Superalloy Based on the Wavelet Neural Network", Applied Mechanics and Materials, Vols. 37-38, pp. 1581-1584, 2010

Online since:

November 2010

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$35.00

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