基于非线性主元分析和符号有向图的故障诊断方法  

A fault diagnosis method based on nonlinear principal component analysis and sign directed graph

作　　者：黄道平[1] 龚婷婷[1] 曾辉[1] 

机构地区：[1]华南理工大学自动化科学与工程学院,广东广州510640

出　　处：《化工学报》2009年第12期3058-3062,共5页CIESC Journal

基　　金：广东省科技计划项目(2003B50301)~~

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.

关 键 词：故障诊断 非线性主元分析 符号有向图 神经网络 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]