Batch process monitoring based on global enhanced multiple neighborhoods preserving embedding

doi:10.1177/01423312211044742

	Batch process monitoring based on global enhanced multiple neighborhoods preserving embedding
	Yao, Hongjuan 1,2; Zhao, Xiaoqiang1,2,3 ; Li, Wei1,2,3 ; Hui, Yongyong1,2,3
	2022-02
发表期刊	TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL
ISSN	0142-3312
卷号	44 期号:3 页码:620-633
摘要	Batch process generally has varying dynamic characteristic that causes low fault detection rate and high false alarm rate, and it is necessary and urgent to monitor batch process. This paper proposes a global enhanced multiple neighborhoods preserving embedding based fault detection strategy for dynamic batch process. Firstly, the angle neighbor is defined and selected to compensate for the insufficient expression for the spatial similarity of samples only by using the distance neighbor, and the time neighbor is introduced to describe the time correlations between samples. These three types of neighbors can fully characterize the similarity of the samples in time and space. Secondly, considering the minimum reconstruction error and the order information of three types of neighbors, an enhanced objective function is constructed to prevent the loss of order information when neighborhood preserving embedding (NPE) calculates the reconstruction weights. Furthermore, the enhanced objective function and a global objective function are organically combined to extract both global and local features, to describe process dynamics and visualize process data in a low-dimensional space. Finally, a monitoring index based on support vector data description is constructed to eliminate adverse effects of non-Gaussian data for monitoring performance. The advantages of the proposed method over principal component analysis, neighborhood preserving embedding, dynamic principal component analysis and time NPE are demonstrated by a numerical example and the penicillin fermentation process simulation.
关键词	Batch process fault monitoring dynamic characteristic neighborhood preserving embedding support vector data description
DOI	10.1177/01423312211044742
收录类别	EI ; SCOPUS ; SCIE
语种	英语
WOS研究方向	Automation & Control Systems ; Instruments & Instrumentation
WOS类目	Automation & Control Systems ; Instruments & Instrumentation
WOS记录号	WOS:000695300200001
出版者	SAGE PUBLICATIONS LTD
EI入藏号	20213810904024
EI主题词	Batch data processing
EI分类号	723.2 Data Processing and Image Processing ; 913.1 Production Engineering ; 921.6 Numerical Methods
来源库	WOS
引用统计	被引频次：5[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/148583
专题	电气工程与信息工程学院
通讯作者	Zhao, Xiaoqiang
作者单位	1.Lanzhou Univ Technol, Coll Elect & Informat Engn, 287,Langongping Load, Lanzhou, Peoples R China; 2.Lanzhou Univ Technol, Key Lab Gansu Adv Control Ind Processes, Lanzhou, Peoples R China; 3.Lanzhou Univ Technol, Natl Expt Teaching Ctr Elect & Control Engn, Lanzhou, Peoples R China
第一作者单位	电气工程与信息工程学院; 兰州理工大学
通讯作者单位	电气工程与信息工程学院; 兰州理工大学
第一作者的第一单位	电气工程与信息工程学院
推荐引用方式 GB/T 7714	Yao, Hongjuan,Zhao, Xiaoqiang,Li, Wei,et al. Batch process monitoring based on global enhanced multiple neighborhoods preserving embedding[J]. TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL,2022,44(3):620-633.
APA	Yao, Hongjuan,Zhao, Xiaoqiang,Li, Wei,&Hui, Yongyong.(2022).Batch process monitoring based on global enhanced multiple neighborhoods preserving embedding.TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL,44(3),620-633.
MLA	Yao, Hongjuan,et al."Batch process monitoring based on global enhanced multiple neighborhoods preserving embedding".TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 44.3(2022):620-633.