Sparse representation preserving embedding based on extreme learning machine for process monitoring
Hui Yongyong1,2; Zhao Xiaoqiang1,2,3
2020-03-17
Source PublicationTRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL
ISSN0142-3312
AbstractExtreme 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
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
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://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.
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.
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 (2020).
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