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	基于贡献率法的非线性工业过程在线故障诊断（英文）
	彭开香 ; 张凯 ; 李钢 ; PENG Kai-Xiang ; ZHANG Kai ; LI Gang
	2016-03-30 ; 2016-03-30
关键词	Kernel principal component analysis(KPCA) nonlinear fault detection contribution rate fault diagnosis TP277
其他题名	Online Contribution Rate Based Fault Diagnosis for Nonlinear Industrial Processes
中文摘要	Over past decades, kernel principal component analysis(KPCA) appeared quite popularly in data-driven process monitoring area. Enormous work has been done to show its simplicity, feasibility, and effectiveness. However, the introduction of kernel trick makes it impossible to directly employ traditional contribution plots for fault diagnosis. In this paper, on the basis of revisiting and analyzing the existing KPCA-relevant diagnosis approaches, a new contribution rate based method is proposed which can explain the faulty variables clearly. Furthermore, a scheme for online nonlinear diagnosis is established. In the end, a case study on continuous stirred tank reactor(CSTR) benchmark is applied to access the effectiveness of the new methodology, where the comparisons with the traditional linear method are involved as well.; Over past decades, kernel principal component analysis(KPCA) appeared quite popularly in data-driven process monitoring area. Enormous work has been done to show its simplicity, feasibility, and effectiveness. However, the introduction of kernel trick makes it impossible to directly employ traditional contribution plots for fault diagnosis. In this paper, on the basis of revisiting and analyzing the existing KPCA-relevant diagnosis approaches, a new contribution rate based method is proposed which can explain the faulty variables clearly. Furthermore, a scheme for online nonlinear diagnosis is established. In the end, a case study on continuous stirred tank reactor(CSTR) benchmark is applied to access the effectiveness of the new methodology, where the comparisons with the traditional linear method are involved as well.
语种	英语 ; 英语
内容类型	期刊论文
源URL	[http://ir.lib.tsinghua.edu.cn/ir/item.do?handle=123456789/147108]
专题	清华大学
推荐引用方式 GB/T 7714	彭开香,张凯,李钢,等. 基于贡献率法的非线性工业过程在线故障诊断（英文）[J],2016, 2016.
APA	彭开香,张凯,李钢,PENG Kai-Xiang,ZHANG Kai,&LI Gang.(2016).基于贡献率法的非线性工业过程在线故障诊断（英文）..
MLA	彭开香,et al."基于贡献率法的非线性工业过程在线故障诊断（英文）".(2016).

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