Nonlinear dynamic process monitoring based on dynamic kernel PCA
Nonlinear dynamic process monitoring based on dynamic kernel PCA
复制标题
DOI：
10.1016/j.ces.2004.07.019
复制
发表时间：
2004-12-01
影响因子：
4.7
通讯作者：
Lee, IB
中科院分区：
文献类型：
--
作者：
Choi, SW;Lee, IB
提出了一种基于动态核主元分析（DKPCA）的非线性动态过程监控方法。核主成分分析（KPCA）中所使用的核函数有利于捕捉过程的非线性特性，而时滞数据扩展则适合描述过程的动态特性。DKPCA使我们能够监控具有严重非线性和（或）动态的任意过程。在这方面，它是多元统计监测方法的广义概念。提出了T-2与SPE相结合的统一监测指标。将所提出的基于DKPCA的监测方法应用于模拟的非线性过程和废水处理过程。比较研究PCA，动态PCA，KPCA，DKPCA的I型错误率，II型错误率，和检测延迟。监测结果证实，拟议的方法产生了最佳的监测性能，即，所有故障的低漏失报警和小检测延迟。(C)2004爱思唯尔有限公司保留所有权利。
Nonlinear dynamic process monitoring based on dynamic kernel principal component analysis (DKPCA) is proposed. The kernel functions used in kernel PCA (KPCA) are profitable for capturing nonlinear property of processes and the time-lagged data extension is suitable for describing dynamic characteristic of processes. DKPCA enables us to monitor an arbitrary process with severe nonlinearity and (or) dynamics. In this respect, it is a generalized concept of multivariate statistical monitoring approaches. A unified monitoring index combined T-2 with SPE is also suggested. The proposed monitoring method based on DKPCA is applied to a simulated nonlinear process and a wastewater treatment process. A comparison study of PCA, dynamic PCA, KPCA, and DKPCA is investigated in terms of type I error rate, type II error rate, and detection delay. The monitoring results confirm that the proposed methodology results in the best monitoring performance, i.e., low missing alarms and small detection delay, for all the faults. (C) 2004 Elsevier Ltd. All rights reserved.