福州大学学报（自然科学版）2011，Vol.39Issue(2)：240-244,5.DOI:CNKI:35-1117/N.20110406.1106.001
基于PCA的火电厂湿法烟气脱硫系统的传感器故障诊断
Study on sensor fau1t diagnosis of wet flue gas desulfurizafion system in thermal power plant based on PCA
摘要
Abstract
A fault diagnosis method using principal component analysis (PCA) is proposed to solve sensor fault diagnosis of wet flue gas desulfurization system in thermal power plant. Based on PCA model, the sensor faults are detected, identified and recovered by calculating square prediction error, sensor validation index, reconstruction index. Employing the actual data from wet flue gas desulfurization system of Huaneng Fuzhou power plant, the PCA model is proved effectively enough to detect and identify the complete invalidation fault, fixed bias fault, drift bias fault and accuracy decrease fault of sensors. The results show that the PCA approach has a good ability in fault diagnosis and recovery.关键词
湿法烟气脱硫系统/传感器/故障诊断/主元分析/故障重构Key words
wet flue gas desulfurizafion system/ sensor/ fault diagnosis/ principal component analysis/ fault reconstruction分类
信息技术与安全科学引用本文复制引用
张丽萍,黄飞群,田丽玲,林心光..基于PCA的火电厂湿法烟气脱硫系统的传感器故障诊断[J].福州大学学报（自然科学版）,2011,39(2):240-244,5.基金项目
福建省发改委产业技术开发资助项目(0803119) （0803119）
