期刊文献+
共找到1篇文章
< 1 >
每页显示 20 50 100
Sensor fault diagnosis of nonlinear processes based on structured kernel principal component analysis 被引量:5
1
作者 Kechang FU Liankui DAI +1 位作者 Tiejun WU Ming ZHU 《控制理论与应用(英文版)》 EI 2009年第3期264-270,共7页
A new sensor fault diagnosis method based on structured kernel principal component analysis (KPCA) is proposed for nonlinear processes. By performing KPCA on subsets of variables, a set of structured residuals, i.e.... A new sensor fault diagnosis method based on structured kernel principal component analysis (KPCA) is proposed for nonlinear processes. By performing KPCA on subsets of variables, a set of structured residuals, i.e., scaled powers of KPCA, can be obtained in the same way as partial PCA. The structured residuals are utilized in composing an isolation scheme for sensor fault diagnosis, according to a properly designed incidence matrix. Sensor fault sensitivity and critical sensitivity are defined, based on which an incidence matrix optimization algorithm is proposed to improve the performance of the structured KPCA. The effectiveness of the proposed method is demonstrated on the simulated continuous stirred tank reactor (CSTR) process. 展开更多
关键词 Sensor fault diagnosis Structured KPCA incidence matrix optimization
下载PDF
上一页 1 下一页 到第
使用帮助 返回顶部