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Deconvolution of Ultrasonic Signals Using a Convolutional Neural Network
Ultrasonics ( IF 4.2 ) Pub Date : 2021-03-01 , DOI: 10.1016/j.ultras.2020.106312
Arthur Chapon , Daniel Pereira , Matthew Toews , Pierre Belanger

Successfully employing ultrasonic testing to distinguish a flaw in close proximity to another flaw or geometrical feature depends on the wavelength and the bandwidth of the ultrasonic transducer. This explains why the frequency is commonly increased in ultrasonic testing in order to improve the axial resolution. However, as the frequency increases, the penetration depth of the propagating ultrasonic waves is reduced due to an attendant increase in attenuation. The nondestructive testing research community is consequently very interested in finding methods that combine high penetration depth with high axial resolution. This work aims to improve the compromise between the penetration depth and the axial resolution by using a convolutional neural network to separate overlapping echoes in time traces in order to estimate the time-of-flight and amplitude. The originality of the proposed framework consists in its training of the neural network using data generated in simulations. The framework was validated experimentally to detect flat bottom holes in an aluminum block with a minimum depth corresponding to λ/4.

中文翻译:

使用卷积神经网络对超声波信号进行反卷积

成功地使用超声波检测来区分紧邻另一个缺陷或几何特征的缺陷取决于超声波换能器的波长和带宽。这解释了为什么在超声检测中通常会增加频率以提高轴向分辨率。然而,随着频率的增加,传播的超声波的穿透深度由于衰减的增加而减小。因此,无损检测研究界对寻找结合高穿透深度和高轴向分辨率的方法非常感兴趣。这项工作旨在通过使用卷积神经网络分离时间轨迹中的重叠回波以估计飞行时间和振幅,从而改善穿透深度和轴向分辨率之间的折衷。所提出的框架的独创性在于它使用模拟中生成的数据训练神经网络。该框架经实验验证可检测铝块中最小深度对应于 λ/4 的平底孔。
更新日期:2021-03-01
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