基于EMD-WP的高精确度特征提取方法

在图像、语音识别或故障诊断等领域，特征提取是关键技术。在研究了小波变换和经验模态分解之后，深入分析了两者在特征提取上的优势和不足，提出了一种将两者优势有效结合来提取特征信息的方法。该方法先将信号做经验模态分解(EMD)得到平稳化单模态分量，再对单模态分量做小波包(WP)分析。最后，通过仿真和实例将本方法和已有文献中的方法进行对比，结果表明该方法不仅具有较高的可行性，而且可以准确地提取特征信息。

High accuracy method of fault extraction based on EMD-WP

Feature extraction is the key technology in the field of image and voice recognition or fault diagnosis. Having deeply studied the advantages and disadvantages of the feature extraction based on the wavelet transform and Empirical Mode Decomposition(EMD), a method combining advantages of the two methods is proposed to extract the feature information. Firstly, the proposed method acquires stabilized single mode state components by EMD, and then Wavelet Packet(WP) analysis is performed to single mode state components. Finally, by comparing the method with other methods through simulation and example test, it is proved that the proposed method not only features higher feasibility, but also can extract fault information more accurately.