摘要
Nonlinear principal component analysis(NLPCA)fault detection method achieves good detection results especially in a nonlinear process.Signed directed graph(SDG)model is based on deep-going information,which excels in fault interpretation.In this work,an NLPCA-SDG fault diagnosis method was proposed.SDG model was used to interpret the residual contributions produced by NLPCA.This method could overcome the shortcomings of traditional principal component analysis(PCA)method in fault detection of a nonlinear process and the shortcomings of traditional SDG method in single variable statistics in discriminating node conditions and threshold values.The application to a distillation unit of a petrochemical plant illustrated its validity in nonlinear process fault diagnosis.
Nonlinear principal component analysis （NLPCA） fault detection method achieves good detection results especially in a nonlinear process. Signed directed graph （SDG） model is based On deepgoing information, which excels in fault interpretation. In this work, an NLPCA-SDG fault diagnosis method was proposed. SDG model was used to interpret the residual contributions produced by NLPCA. This method could overcome the shortcomings of traditional principal component analysis （PCA） method in fault detection of a nonlinear process and the shortcomings of traditional SDG method in single variable statistics in discriminating node conditions and threshold values. The application to a distillation unit of a petrochemical plant illustrated its validity in nonlinear process fault diagnosis.
出处
《化工学报》
EI
CAS
CSCD
北大核心
2009年第12期3058-3062,共5页
CIESC Journal
基金
广东省科技计划项目(2003B50301)~~
关键词
故障诊断
非线性主元分析
符号有向图
神经网络
fault diagnosis
nonlinear principal component analysis
signed directed graph
neural network
