在钢结构脉冲涡流缺陷识别中，通常采用信号的峰值幅度、过零时间、主峰面积等特征参数对缺陷进行表征。但上述参数相互关联，存在一定的信息冗余，增加了数据分析量及信息筛选难度，进而影响了缺陷识别的效率。针对上述问题，采用主成分分析法对脉冲涡流信号的6个特征参数进行降维处理，构造了一个主成分特征，减少了信息冗余；将上述主成分特征输入Logistic分类器，实现了对钢结构减薄缺陷的准确识别。结果表明：主成分分析法可以在确保缺陷识别准确率的情况下，有效减少分类器处理的数据量，提高缺陷识别效率。

In the pulsed eddy current testing (PEC) of steel structures, multiple characteristics such as peak amplitude, zero-cross time, and peak area etc., are usually used for defect characterization. However, information redundancy existing among the above characteristics increases the amount of data for the analysis and the difficulty of information filtering; this could influence the efficiency of defect identification. In order to reduce information redundancy, principal component analysis (PCA) was employed to compress the six characteristics into one principal component characteristic which was then used as input for Logistic classifier to identify thinning defects in steel structure. The results show that PCA can effectivly reduce the amount of data processed by classifier and improve the efficiency of defect identification while ensuring accuracy.