针对采用幅值、能量等统计特征参数分类识别声发射(AE)信号时存在的信息冗余问题，提出利用主成分分析(PCA)方法减少信息冗余，提取AE信号统计特征。设计了钢板表面铬层裂纹试验，对统计特征参数进行主成分分析，提取了两个主成分。设计了支持向量机(SVM)分类器，以主成分为输入向量，分类识别铬层裂纹AE信号。验证了主成分可以有效表征AE信号统计特征，减少了信息冗余，提高了分类效率及准确率。

Information redundancy is a big problem in acoustic emission(AE) signal identification based on statistical feature parameters such as amplitude, energy counts, etc. Here, principle component analysis(PCA) was employed to reduce information redundancy and extract statistical feature of AE signals.The AE data were collected in the AE test for Cr-coating cracking on the surface of a steel plate, AE statistical feature parameters were analyzed using PCA, and two principle components were extracted. The principle components were employed as the input vector of a SVM classifier, and the AE signals caused by Cr-coating cracking were identified . It demonstrated that principle components could represent statistical feature of AE signals, reduce information redundancy, and effectively raise identification efficiency and accuracy.