The Ultrasonic Signal Identification of the Nickel-Based Superalloy Based on the Wavelet Neural Network
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.
Yi-Min Deng, Aibing Yu, Weihua Li and Di Zheng
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