Nonlinear Process Monitoring Based on Improved Kernel ICA

摘要：

An industrial process often presents a large number of measured variables, which are usually driven by fewer nonlinear essential variables. An improved kernel independent component analysis based on particle swarm optimization (PSO-KICA) is presented to extract these essential variables from the process recorded variables in the KPCA feature space. Process faults can be detected more e±ciently by monitoring the independent components. To monitor the non-Gaussian distributed independent components obtained by PSO-KICA, the empirical control limit is employed. The proposed approach is illustrated by the application to the nonisothermal CSTR process.

作者: Lei Xie Shuqing Wang

作者单位: Institute of Advanced Process Control,Zhejiang University,China

会议类型: 国际会议

会议名称: 第四届亚洲过程系统工程会议暨2007年中国国际系统工程年会(The 4th International Symposium on Design,Operation & Control of Chemical Processes)(PSE ASIA 2007)

会议地点: 西安

会议语种:英文

在线出版日期: 2007-08-15（万方平台首次上网日期，不代表论文的发表时间）