CAJ | 학술논문

提出一种基于小波包变换（wavelet packets transform, WPT）与核主成分分析（kernel principal component analysis，KPCA）的颤振识别方法。铣削颤振会抑制或增强某些频段内的信号，利用四层小波包分解与重构，得到16个频段内的重构信号，获得各重构信号的面积，并进行归一化处理，完成铣削颤振特征向量的选择。继而通过对比基于主成分分析（principal component analysis，PCA）与核主成分分析的特征提取方法的特征提取效果，选取KPCA对特征向量进行降维处理，最后以降维后的数据作为最小二乘支持向量机分类器的输入对铣削状态进行识别。结果表明，在小样本的情况下仍能有效、准确地对铣削状态进行分类，分类准确率达95.0%。
제출일충기우소파포변환（wavelet packets transform, WPT）여핵주성분분석（kernel principal component analysis，KPCA）적전진식별방법。선삭전진회억제혹증강모사빈단내적신호，이용사층소파포분해여중구，득도16개빈단내적중구신호，획득각중구신호적면적，병진행귀일화처리，완성선삭전진특정향량적선택。계이통과대비기우주성분분석（principal component analysis，PCA）여핵주성분분석적특정제취방법적특정제취효과，선취KPCA대특정향량진행강유처리，최후이강유후적수거작위최소이승지지향량궤분류기적수입대선삭상태진행식별。결과표명，재소양본적정황하잉능유효、준학지대선삭상태진행분류，분류준학솔체95.0%。
A milling chatter identification method based on wavelet packet transform (WPT) and kernel principal component analysis (KPCA) is proposed. This method consists of four steps. In the first step, the measured milling force signals are decomposed and reconstructed by four-level WPT, so that the force signals can be allocated in a certain frequency band. In the second step, the reconstructed signal areas of different frequency bands are normalized and selected as a feature vector. In the third step, through the mutual comparison of the results of principal component analysis (PCA) method and KPCA method, the KPCA feature extraction method is selected for dimension reduction. Finally, the least squares support vector machine (LS-SVM) is designed for normal milling and chatter pattern classification. The experimental results prove that the method can identify the chatter accurately and effectively even in the case of small number of samples with an accuracy rate of 95.0%.