中文摘要：

在巨磁阻脉冲涡流传感器（GMR—PEC）上实现平板导体表面和次表面裂纹缺陷以及孔缺陷进行精确分类。在频率分析基础上，提出了一种新的缺陷特征量——涡流差分响应信号的功率谱密度。由于主成分分析具有良好的降维特性，采用主成分分析结合线性判别分类（PCA-LDA）和贝叶斯分类（PCA-13ayes）进行缺陷的分类。结果表明，基于新的特征量的分类方法能实现导体表面和次表面的裂纹和孔缺陷的精确分类，在脉冲涡流自动测量领域具有潜在的意义。

英文摘要：

The main objective of this study aims to precisely classify the cracks and cavities in surface and subsurface by using features-based giant-magneto-resistive pulsed eddy current （GMR-PEC） sensor. A new defect feature named as the power spectral density analysis of the direct differential PEC response is carried out based-on the amplitude spectrum. Principal component analysis is designed to reduce the dimensional index with the ability of supplying the lower dimensional feature. The PCA combined linear discriminatary analysis （PCA-LDA） and the Bayesian classifier （PCA-Bayes） are both applied for defect classification. Consequently, the experimental results demonstrate that the cracks and cavities in surface and sub-surface can he classified satisfactorily by the proposed methods using the new feature, which have the potential for gauging automatic in-situ inspection for PEC.