期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 34, 期 2, 页码 1028-1037出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2006.10.029
关键词
classification; fault diagnosis; kernel discriminant analysis; decision tree; orthogonal filter
The early detection and reliable diagnosis of a fault is crucial in an on-going operation of processes. They provide early warning for a fault and identification of its assignable cause. This paper proposes a classification tree-based diagnosis scheme combined with nonlinear kernel discriminant analysis. The nonlinear kernel-based dimension reduction for the discrimination of various classes of data is performed to determine nonlinear decision boundaries. The use of the nonlinear kernel method in a classification tree is to reduce the dimension of data and to provide its lower-dimensional representation suitable for separating different classes. We also present the use of orthogonal filter as a preprocessing step. An orthogonal filter-based preprocessing is performed to remove unwanted variation of data for enhancing discrimination power and classification performance. The performance of the proposed method is demonstrated using simulation data and compared with other methods. The classification results showed that the proposed tree-based method outperforms traditional PCA-based method. (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