南京理工大学学报（自然科学版）2019，Vol.43Issue(1)：72-77,85,7.DOI:10.14177/j.cnki.32-1397n.2019.43.01.010
改进主元分析方法及数据重构在工业系统中的故障诊断研究
Research on fault diagnosis of industrial process based on improved PCA method and data reconstruction
摘要
Abstract
Not only in order to determine the fault more accurately in the industrial system,but also in order to make the production system operation more stable, the improved principal component analysis method and data reconstruction method is used in the industrial process. The data of the normal and fault state of industrial system are collected,the SPE statistics of the traditional principal component analysis is divided into principal-component-related variable residual( PVR) and common variable residua( CVR) ,which are used to diagnose the system. In order to minimize the impact of the failure data on the system after detecting the failure,the data reconstruction method is further applied. The failure data are reconstituted into normal data,and the validity index is used to verify. When the fault happenes,the fault is repaired and excluded,and the failure impact on the production system is minimized. In order to verify the diagnosis method,the method is applied to the data of the Tennessee-Eastman system, the detection result of the fault is more precise, and the normal production system is ensured to work.关键词
主元分析/故障分析/故障重构/主元显著关联的检测残差变量/一般变量残差/生产安全Key words
principal component analysis/ fault analysis/ fault reconstruction/ principal-component-related variable residual/common variable residual/production safety分类
信息技术与安全科学引用本文复制引用
杜海莲,苗诗瑜,杜文霞,吕锋..改进主元分析方法及数据重构在工业系统中的故障诊断研究[J].南京理工大学学报（自然科学版）,2019,43(1):72-77,85,7.基金项目
国家自然科学基金(61673160 （）
60974063 （）
61175059) （）
河北省自然科学基金(F2014205115) （F2014205115）
河北省教育厅课题(ZD2016053 （）
QN2018087) （）
