期刊
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
卷 87, 期 9-12, 页码 3425-3436出版社
SPRINGER LONDON LTD
DOI: 10.1007/s00170-016-8745-7
关键词
KPCA; MWKPCA; AKPCA; Variable Moving Window Kernel PCA (VMWKPCA); Fault detection
Kernel Principal Component Analysis (KPCA) is a nonlinear extension of Principal Component Analysis (PCA). Recently, it is the most popular technique for monitoring nonlinear processes. However, the time-varying property of the industrial processes requires the adaptive ability of the KPCA. Therefore, in this paper, a Variable Moving Window Kernel PCA (VMWKPCA) method is proposed to update the KPCA model. The concept of this method consists of varying the size of the moving window according to the change of the normal process. To evaluate the performance of the proposed method, the VMWKPCA is applied for monitoring a Continuous Stirred Tank Reactor (CSTR) and a Tennessee Eastman process (TE). The results are satisfactory compared to the conventional Moving Window Kernel PCA (MWKPCA) and the Adaptive Kernel PCA (AKPCA).
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