分形理论在近表面缺陷超声A扫检测中的应用

针对超声相控阵无损检测(NDT)中近表面缺陷识别困难的问题,提出一种基于分形理论的近表面缺陷智能识别方法。运用基于线性插值的盒计数维数算法,计算140组超声A扫(A-Scan)信号的盒计数维数,并运用统计的方法详细分析其分布情况。实验结果表明超声A-scan信号具有分形特性,分形理论可应用于A-Scan信号分析;而且有无缺陷信号的盒维数分布区间差异明显,盒维数可作为A-Scan信号的特征识别近表面缺陷。在超声相控阵自动化检测中,运用分形理论能提高近表面缺陷的检出率，减少人为因素引起的漏检。

Application of fractal theory on identification of near-surface defects in ultrasonic A-Scan detection

The near-surface defects are hard to identify in ultrasonic phased array Non-Destructive Testing (NDT), thus a new intelligent identification method based on fractal theory was proposed to solve this problem. A box-counting dimension algorithm based on linear interpolation was described to calculate the box-counting dimension of 140 groups of ultrasonic A-Scan time domain signals. Then the distribution of box-counting dimension was analyzed using the statistical method. The experimental results show that ultrasonic A-Scan signal is obviously fractal and it is effective to analyze the A-Scan signal with the fractal approach. This method has the potential to identify near-surface defects since the values of the box counting dimension of defective signals are different from those of defective signals. As a result, the detection rate of near-surface defects can be improved and the omission rate caused by man-made factors can be reduced in ultrasonic phased array automatic testing.