Bettayeb Fairouz, Rachedi Tarek, Benbartaoui Hamid
CSC, Research Center on Welding and Control, Route de Dely Brahin, BP: 64, Chéraga, Algiers, Algeria.
Ultrasonics. 2004 Apr;42(1-9):853-8. doi: 10.1016/j.ultras.2004.01.064.
Despite of the widespread and increasing use of digitized signals, the ultrasonic testing community has not realized yet the full potential of the electronic processing. The performance of an ultrasonic flaw detection method is evaluated by the success of distinguishing the flaw echoes from those scattered by microstructures. So, de-noising of ultrasonic signals is extremely important as to correctly identify smaller defects, because the probability of detection usually decreases as the defect size decreases, while the probability of false call does increase. In this paper, the wavelet transform has been successfully experimented to suppress noise and to enhance flaw location from ultrasonic signal, with a good defect localization. The obtained result is then directed to an automatic Artificial Neuronal Networks classification and learning algorithm of defects from A-scan data. Since there is some uncertainty connected with the testing technique, the system needs a numerical modelling. So, knowing the technical characteristics of the transducer, we can preview which are the defects that experimental inspection should find. Indeed, the system performs simulation of the ultrasonic wave propagation in the material, and gives a very helpful tool to get information and physical phenomena understanding, which can help to a suitable prediction of the service life of the component.
尽管数字化信号的使用广泛且不断增加，但超声检测界尚未充分认识到电子处理的全部潜力。超声探伤方法的性能是通过能否成功区分缺陷回波与微观结构散射回波来评估的。因此，超声信号的去噪对于正确识别较小缺陷极为重要，因为检测概率通常会随着缺陷尺寸减小而降低，而误判概率则会增加。本文成功地运用小波变换来抑制噪声并从超声信号中增强缺陷定位，实现了良好的缺陷定位。然后将所得结果用于基于A扫描数据的缺陷自动人工神经网络分类和学习算法。由于检测技术存在一定的不确定性，该系统需要进行数值建模。因此，了解换能器的技术特性后，我们可以预先知道实验检测应该发现哪些缺陷。实际上，该系统对材料中的超声波传播进行模拟，并提供了一个非常有用的工具来获取信息和理解物理现象，这有助于对部件的使用寿命进行合理预测。
