关键词: 稀疏核主元分析, 在线监控, 非线性过程

Abstract:

Kernel principal component analysis (KPCA) is suitable for nonlinear process monitoring, but it suffers many limitations, such as great calculation load, and poor real time performance. A monitoring method based on sparse KPCA (SKPCA) was proposed to decrease calculation load and improve real time monitoring. SKPCA was firstly used to weight the normal modeling data, and the minority of data with high weight could basically represent the information of the whole data, so modeling data could be largely reduced. Following this, KPCA model and the monitoring indices were built based on the sparse modeling data. In the end, taking a chemical separation process for example, KPCA and SKPCA were compared in terms of monitoring result and real time performance, and the superiority of the proposed SKPCA method was demonstrated.

Key words: 稀疏核主元分析, 在线监控, 非线性过程