利用深度神经网络和小波包变换进行缺陷类型分析

超声检测中对缺陷进行类型分析通常取决于操作人员对于特定专业知识的了解及检测经验,从而导致其分析结果的不稳定性和个体差异性。本文提出了一种使用小波包变换提取缺陷特征信息,并应用深度神经网络对得到的信息进行分类识别的方法。利用超声相控阵系统对于不锈钢试块上的通孔、斜通孔和平底孔进行超声检测,并对得到的超声回波波形按照新方法进行分析。实验结果表明,使用小波包变换后的数据进行分类识别能够在提高识别准确率的同时降低神经网络的学习时间,而使用深度神经网络相比通用的BP神经网络以可接受延长学习时间的代价提高了识别的准确率。采用新方法后,缺陷分类正确率提高了21.66%,而网络学习时间只延长了91.9s。在超声检测中使用小波包变换和深度神经网络来对于缺陷进行类型分析,能够排除人为干扰,增加识别准确率,对于实际应用有着极大的意义。 

Analysis on defect classification by deep neural networks and wavelet packet transform

Defect classification analysis in ultrasonic detection usually depends on the operator's professional knowledge and experience, which leads to the instability and individual differences of the analysis result. To solve the problem, a method for classification analysis of ultrasonic detection signals is presented. The method uses wavelet packet transform to extract the feature information from ultrasonic defection, and applies deep neural networks to classification. Experiments are tested by ultrasonic phased array system to collect detection signal which comes from via holes, inclined holes or flat-bottom holes. Then, classification analysis is done by the new method. Experimental results show two points. The first point is that using wavelet packet transform can increase the recognition accuracy and decrease the learning time of neural networks. The second point is that using deep neural networks can increase the recognition accuracy while increase acceptable learning time comparing to the common BP neural networks. By using the new method, the defect classification accuracy increases up 21.66% while the learning time increases only 91.9 s. Applying wavelet packet transform and deep neural networks to classification analysis in ultrasonic detection can exclude human interference and improve the recognition accuracy. As so, the new method has a great future in practical applications of ultrasonic detection.