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Wavelet Neural Network for Ultrasonic Detection of Materials with Coarsegrained
Author: SongYuLing
Tutor: ChengJianZheng
School: Wuhan Institute of Physics and Institute of Mathematics
Course: Acoustics
Keywords: coarsegrained material wavelet neural network ultrasonic detection scattered wave
CLC: O426
Type: Master's thesis
Year: 2005
Downloads: 135
Quote: 1
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Abstract
In ultrasonic detecting of coarsegrained material specimen or workpiece withlimited size, there are many kinds of noise such as boundary reflection waves andacousticelectric crosstalk signals in received signals besides the scattered waves, flawecho and bottom echo. In this paper, first, the origin of the noise signals anddenoising method have been researched experimentally. Then, the primarycharacteristics of the scattered wave, such as the earliest time it arrives to receivingtransducer, the position of its maximum value and its frequency spectrumcharacteristic in the propagation process have been verified experimentally. Amongtheoretical analysis, we calculate the sound pressure distribution along axis usingpulse frequency spectrum composition method for transmitting and receiving complexfield. According to above calculation we give a physical explanation to the position ofthe maximum pressure value, We also use short time Fourier transform to analyzethe spectrum of the scattered wave during the propagation process, and obtain someuseful results.Because of the scattering characteristic of the coarsegrained material, theuseful signals are covered up by the scattered waves which come from themicrostructures of the material. In order to minimize the influence of the scatteredwaves on the useful signals and improve the signaltonoise ratio (SNR), waveletneural network is applied. Wavelet neural network is a nonlinear filtering methodthat can be used to reduce the noise of ultrasonic signals adaptively. Improvedgradient descent algorithm with gauss mother wavelet has been used to train theneural network. In the process of training, the network has a dynamic adaptivelearning rate, which can be updated automatically according to gradient descent. The experimental results show that wavelet neural network is an effective method indenoising of ultrasonic signals. With improved gradient descent algorithm and gaussmother wavelet, the network has completed training in 21 times with the error0.001177. Compared with the normal gradient descent algorithm, the learning speedof the procedure described here is evident increased. It effectively avoids convergingat a local minimum. These advantages show that wavelet neural network indenoising of ultrasonic signals is a quite useful tool in preprocessing stage in flawdetection. It has a great significance in identifying useful signals and accomplishingreal time processing in ultrasonic detection.

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CLC: > Mathematical sciences and chemical > Physics > Acoustics > Ultrasonics
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