An improved feature parameter extraction algorithm is proposed in this study to solve the problem of quantitative detection of subsurface defects. Firstly, the common feature parameters from the differential signal of pulsed eddy current and ultrasonic are extracted in time domain and frequency domain. Then, the dispersion model and ReliefF model are established to determine the weights of each parameter. Finally, the weights from the two different algorithms are fused by the D-S evidence theory to determine feature parameters. Compared with the PCA feature parameter algorithm from the pulsed eddy current or ultrasonic, the experiment results show the feature parameters extracted by the algorithm proposed in this paper are more effective in quantitative detection of subsurface defects. It will lead to high accuracy in the subsurface defections.

中文翻译：

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基于融合理论的复合检测方法改进的特征参数提取算法

为了解决地下缺陷的定量检测问题，提出了一种改进的特征参数提取算法。首先，从时域和频域提取脉冲涡流和超声差分信号的共同特征参数。然后，建立色散模型和ReliefF模型以确定每个参数的权重。最后，DS证据理论将两种不同算法的权重融合在一起，以确定特征参数。实验结果表明，与脉冲式涡流或超声方法中的PCA特征参数算法相比，本文提出的算法提取的特征参数在定量检测地下缺陷方面更为有效。这将导致地下缺陷的高精度。