期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 33, 期 1, 页码 241-250出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2006.04.020
关键词
fault diagnosis; kernel independent component analysis; kernel principal component analysis; support vector machines; feature extraction
Recently, principal components analysis (PCA) and independent components analysis (ICA) was introduced for doing feature extraction. PCA and ICA linearly transform the original input into new uncorrelated and independent features space respectively. In this paper, the feasibility of using nonlinear feature extraction is studied and it is applied in support vector machines (SVMs) to classify the faults of induction motor. In nonlinear feature extraction, we employed the PCA and ICA procedure and adopted the kernel trick to nonlinearly map the data into a feature space. A strategy of multi-class SVM-based classification is applied to perform the faults diagnosis. The performance of classification process due to various feature extraction method and the choice of kernel function is presented and compared to show the excellent of classification process. (c) 2006 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据