Quality-related fault diagnosis based on k-nearest neighbor rule for non-linear industrial processes

doi:10.1177/15501477211055931

CORC > 自动化研究所 > 中国科学院自动化研究所 > 精密感知与控制研究中心

	Quality-related fault diagnosis based on k-nearest neighbor rule for non-linear industrial processes
	Ren, Zelin 1,2; Tang, Yongqiang 2; Zhang, Wensheng 1,2
刊名	INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS
	2021-11-01
卷号	17 期号:11 页码:14
关键词	Fault detection fault diagnosis quality-related non-linear industrial process k-nearest neighbor rule
ISSN号	1550-1477
DOI	10.1177/15501477211055931
通讯作者	Zhang, Wensheng(zhangwenshengia@hotmail.com)
英文摘要	The fault diagnosis approaches based on k-nearest neighbor rule have been widely researched for industrial processes and achieve excellent performance. However, for quality-related fault diagnosis, the approaches using k-nearest neighbor rule have been still not sufficiently studied. To tackle this problem, in this article, we propose a novel quality-related fault diagnosis framework, which is made up of two parts: fault detection and fault isolation. In the fault detection stage, we innovatively propose a novel non-linear quality-related fault detection method called kernel partial least squares-k-nearest neighbor rule, which organically incorporates k-nearest neighbor rule with kernel partial least squares. Specifically, we first employ kernel partial least squares to establish a non-linear regression model between quality variables and process variables. After that, the statistics and thresholds corresponding to process space and predicted quality space are appropriately designed by adopting k-nearest neighbor rule. In the fault isolation stage, in order to match our proposed non-linear quality-related fault detection method kernel partial least squares-k-nearest neighbor seamlessly, we propose a modified variable contributions by k-nearest neighbor (VCkNN) fault isolation method called modified variable contributions by k-nearest neighbor (MVCkNN), which elaborately introduces the idea of the accumulative relative contribution rate into VC k-nearest neighbor, such that the smearing effect caused by the normal distribution hypothesis of VC k-nearest neighbor can be mitigated effectively. Finally, a widely used numerical example and the Tennessee Eastman process are employed to verify the effectiveness of our proposed approach.
资助项目	National Natural Science Foundation of China[61976213] ; National Natural Science Foundation of China[61772525] ; National Natural Science Foundation of China[61906191]
WOS关键词	COMPONENT ANALYSIS
WOS研究方向	Computer Science ; Telecommunications
语种	英语
出版者	SAGE PUBLICATIONS INC
WOS记录号	WOS:000726705600001
资助机构	National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/46572]
专题	自动化研究所_精密感知与控制研究中心
通讯作者	Zhang, Wensheng
作者单位	1.Univ Chinese Acad Sci, Beijing, Peoples R China 2.Chinese Acad Sci, Res Ctr Precis Sensing & Control, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Ren, Zelin,Tang, Yongqiang,Zhang, Wensheng. Quality-related fault diagnosis based on k-nearest neighbor rule for non-linear industrial processes[J]. INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS,2021,17(11):14.
APA	Ren, Zelin,Tang, Yongqiang,&Zhang, Wensheng.(2021).Quality-related fault diagnosis based on k-nearest neighbor rule for non-linear industrial processes.INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS,17(11),14.
MLA	Ren, Zelin,et al."Quality-related fault diagnosis based on k-nearest neighbor rule for non-linear industrial processes".INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 17.11(2021):14.