化工学报2008，Vol.59Issue(7)：1783-1789,7.
基于特征样本核主元分析的TE过程快速故障辨识方法
Fault identification of Tennessee Eastman process based on FS-KPCA
摘要
Abstract
For several complex industry processes,the original fault sources are difficult to identify by using kernel principal component analysis(kernel PCA)methods.And during the modeling and online dynamic monitoring process,the calculation of the kernel matrix K is a bottleneck problem for a large data set.An integrated fault diagnosis method based on feature sample extracting and kernel PCA was developed.Firstly,a feature extraction method was adopted to pre-process the modeling data set for solving the calculation problem of the kernel matrix K.Secondly,Hotelling statistics,T2 and SPE of kernel PCA were adopted to detect system fault.Once fault was detected,the gradient algorithm of kernel function was used to define two new statistics,CT2 and CSPE,which represented the contribution of each variable to Hotelling T2 and SPE respectively.According to the degree of contribution,the fault variables might be identified from these correlative variables.To demonstrate the performance,the proposed method was applied to the Tennessee Eastman(TE)process.The simulation results showed that the proposed method could effectively identify various types of fault sources.关键词
核主元分析/故障辨识/梯度算法/特征样本提取/TE过程Key words
kernel PCA/fault identification/gradient arithmetic/feature sample extracting/TE process分类
信息技术与安全科学引用本文复制引用
薄翠梅,张湜,张广明,王执铨..基于特征样本核主元分析的TE过程快速故障辨识方法[J].化工学报,2008,59(7):1783-1789,7.基金项目
supported by the National Natural Science Foundation of China(60574082),the Natural Science Foundation of Jiangsu Province(BK2006176),and the Jiangsu Province Higher Education Natural Science Foundation(07KJB510042). （60574082）
