中国计量学院学报2012，Vol.23Issue(4)：332-337,6.
核主成分分析的高炉故障检测研究
Blast furnace fault detection based on KPCA
摘要
Abstract
Traditional fault detection methods are ineffective in complex blast furnace processes. On the other hand, the data in the blast furnace process had significant nonlinear characteristics; and in PCA and other multiple linear statistical methods it is also difficult to obtain good fault detection effect. We proposed a method to detect fault in the blast furnace process based on the KPCA method. It fully adapts to blast furnaces' nonlinear characteristics to achieve fast and accurate detection of blast furnace failures.关键词
高炉冶炼/故障检测/主成分分析/核主成分分析Key words
blast furnace smelting/sensor/fault detections PCA/KPCA分类
信息技术与安全科学引用本文复制引用
孟程程,曾九孙,李文军..核主成分分析的高炉故障检测研究[J].中国计量学院学报,2012,23(4):332-337,6.基金项目
国家自然科学基金项目资助(No.61203088). （No.61203088）
