Fault Diagnosis for Dynamic Nonlinear System Based on Kernel Principal Component Analysis

摘要：

Kernel principal component analysis is a type of nonlinear principal component analysis, to decouple the nonlinear correlation of variables by using kernel functions and integral operators, and by computing the principal components in the high dimensional feature space. A method of fault diagnosis for dynamic nonlinear system by dynamic kernel principal component analysis is presented in this paper, and the root of fault causes is isolated by the reconstructed variables with nonlinear least squares optimization. The simulations in the continuous stirred-tank reactor (CSTR) indicate that the performances of process monitoring and fault diagnosis by this presented method are superior to that by kernel principal component analysis.

作者: Yanwei Huang Xianbo Qiu

作者单位: College of Electrical Engineering & Automation, Fuzhou University, Fuzhou, 350108, China Department of Mechanical Engineering & Applied Mechanics, University of Pennsylvania, USA

会议类型: 国际会议

会议名称: The Second International Joint Conference on Computational Science and Optimization(CSO 2009)(2009 国际计算科学与优化会议)

会议地点: 三亚

会议语种:英文

页码: 1730-1733

在线出版日期: 2009-04-24（万方平台首次上网日期，不代表论文的发表时间）