基于EMD及主成分分析的缺陷超声信号特征提取研究

Research on feature extraction of ultrasonic flaw signal based on EMD and principal component analysis

针对非线性、非平稳超声缺陷信号的特征提取问题,提出一种经验模态分解(EMD)和主成分分析(PCA)相结合的缺陷信号特征提取方法。对缺陷信号进行EMD分解得到本征模态函数(IMF),根据能量比率累积选取IMF,平均截取傅里叶变换后的各模态频谱得到能表征原信号的特征向量集;构建PCA模型,特征向量集降维得到低维特征向量,该过程可降低缺陷信号分析数据的复杂度和冗余度,以BP神经网络为缺陷分类器对缺陷特征进行识别与分类。实验结果表明该方法具有可靠的识别与分类效果。

Aiming at the feature extraction of non-linear and non-stationary ultrasonic flaw signal,a feature extraction method combining EMD and PCA was proposed. The flaw signal was decomposed to obtain IMF, and IMF was selected according to the energy ratio accumulation, and the spectrum of each modal after the Fourier transform was averaged to obtain the eigenvector of original signal. The PCA model was constructed and the dimension reduction of eigenvector set was achieved to obtain lower-dimension eigenvector, which reduced the data complexity and redundancy of the flaw signal analysis. Besides, BP neural network was used as the flaw classifier to identify and classify the defect features. The test results show that the proposed method has reliable flaw detection and classification effect on flaw signal.