Sparse representation preserving embedding based on extreme learning machine for process monitoring
Hui Yongyong1,2; Zhao Xiaoqiang1,2,3
2020-06
Source PublicationTRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL
ISSN0142-3312
Volume42Issue:10Pages:1895-1907
Abstract

Extreme learning machine (ELM) is a fast learning mechanism used in many domains. Unsupervised ELM has improved to extract nonlinear features. A nonlinear dynamic process monitoring method named sparse representation preserving embedding based on ELM (SRPE-ELM) is proposed in this paper. First, the noise is removed by sparse representation and the sparse coefficient is applied to construct the adjacency graph. The adjacency graph with a data-adaptive neighborhood can extract dynamic manifold structure better than a specified neighborhood parameter. Secondly, a new objection function considered the sparse reconstruction and output weights is established to extract nonlinear dynamic manifold structure. Thirdly, the statistic SPE and T-2 based on SRPE-ELM are built to monitor the whole process. Finally, SRPE-ELM is applied in the IRIS data classification example, a numerical case and Tennessee Eastman benchmark process to verify the effectiveness of process monitoring.

KeywordProcess monitoring extreme learning machine neighborhood preserving embedding sparse representation
DOI10.1177/0142331219898937
Indexed BySCI ; SCIE ; EI
Language英语
Funding ProjectNational Natural Science Foundation of China[61763029][61873116] ; National Defense Basic Research Project of China[JCKY2018427C002] ; Industrial support and guidance project of colleges and universities in Gansu Province[2019C-05] ; Open fund project of Key Laboratory of Gansu Advanced Control for Industrial Processes[2019KFJJ05]
WOS Research AreaAutomation & Control Systems ; Instruments & Instrumentation
WOS SubjectAutomation & Control Systems ; Instruments & Instrumentation
WOS IDWOS:000523847100001
PublisherSAGE PUBLICATIONS LTD
EI Accession Number20201308348685
EI KeywordsProcess monitoring
EI Classification Number723.4 Artificial Intelligence - 913.1 Production Engineering
Source libraryWOS
Citation statistics
Cited Times:6[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttps://ir.lut.edu.cn/handle/2XXMBERH/64315
Collection电气工程与信息工程学院
Corresponding AuthorZhao Xiaoqiang
Affiliation1.Lanzhou Univ Technol, Coll Elect & Informat Engn, Lanzhou 730050, Peoples R China;
2.Lanzhou Univ Technol, Key Lab Gansu Adv Control Ind Proc, Lanzhou, Peoples R China;
3.Lanzhou Univ Technol, Natl Expt Teaching Ctr Elect & Control Engn, Lanzhou, Peoples R China
First Author AffilicationColl Elect & Informat Engn;  Lanzhou University of Technology
Corresponding Author AffilicationColl Elect & Informat Engn;  Lanzhou University of Technology
First Signature AffilicationColl Elect & Informat Engn
Recommended Citation
GB/T 7714
Hui Yongyong,Zhao Xiaoqiang. Sparse representation preserving embedding based on extreme learning machine for process monitoring[J]. TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL,2020,42(10):1895-1907.
APA Hui Yongyong,&Zhao Xiaoqiang.(2020).Sparse representation preserving embedding based on extreme learning machine for process monitoring.TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL,42(10),1895-1907.
MLA Hui Yongyong,et al."Sparse representation preserving embedding based on extreme learning machine for process monitoring".TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 42.10(2020):1895-1907.
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