郑州大学学报（理学版）Issue(4)：113-118,6.DOI:10.3969/j.issn.1671-6841.2015.04.022
石油钻井过程故障检测的多模核主元分析方法
Multimode Kernel Principal Component Analysis Method of Drilling Process Fault Detection
摘要
Abstract
A kernel principal component analysis ( KPCA ) method applicable to the drilling process fault detection was put forward. Firstly, process data were classified by using threshold classification algo-rithm, and the data of the steady state condition were obtained. Secondly, according to the classification data the corresponding KPCA model was established, and these corresponding KPCA models were com-bined together to realize fault detection. After multiple tests, the method was proved to be suitable for fault detection of drilling process, the detection sensitivity was improved and the error was reduced.关键词
门限值分类/变工况过程/核主元分析/故障检测Key words
threshold classification/varying working condition/kernel principal component analysis/fault detection分类
信息技术与安全科学引用本文复制引用
王杰,李璐..石油钻井过程故障检测的多模核主元分析方法[J].郑州大学学报（理学版）,2015,(4):113-118,6.基金项目
国家自然科学基金资助项目,编号61473266 （）
