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A key performance indicator-relevant approach based on kernel entropy component regression model for industrial system
Sun, Chengyuan1; Kang HB(康浩博)2; Ma HJ(马宏军)1; Bai, Hua1
Department智能检测与装备研究室
Source PublicationOPTIMAL CONTROL APPLICATIONS & METHODS
ISSN0143-2087
2021
Pages1-16
Indexed BySCI ; EI
EI Accession number20213510839808
WOS IDWOS:000691121000001
Contribution Rank2
Funding OrganizationFundamental Research Funds for the Central UniversitiesFundamental Research Funds for the Central Universities [N2004018] ; National Key Research and Development Program of China [SQ2019YFE020319] ; National Science of Foundation China [61420106016, 6162100, 61873306, U1908213] ; State Key Laboratory of Synthetical Automation for Process Industries [2018ZCX19, SAPI2019-3] ; Zhejiang Lab [2019NB0AB07]
Keywordfault detection KECA KECR KPI-relevant
Abstract

Key performance indicator (KPI)-relevant fault detection method has been raised for decades to hugely increase the economic interest of modern industries. However, the typical data-driven approaches like the kernel principal component analysis (KPCA) and the kernel entropy analysis (KECA) are inefficient to consider the influence taken by the fault factor on the KPI. Thus, in this work, an algorithm called the kernel entropy regression (KECR) is proposed to enhance the interpretability between the fault and the KPI. The proposed algorithm captures the information relevant to the KPI state in the subspace and rewords the decomposition of the KECA method. The angular structure of the KECR method achieves an accurate partition for process variables to hugely decrease false detection results. In the end, an industrial case is utilized to demonstrate the effectiveness of the KECR method.

Language英语
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/29552
Collection智能检测与装备研究室
Corresponding AuthorKang HB(康浩博)
Affiliation1.College of Information Science and Engineering, Northeastern University, Shenyang, China
2.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning Province 110169, China.
Recommended Citation
GB/T 7714
Sun, Chengyuan,Kang HB,Ma HJ,et al. A key performance indicator-relevant approach based on kernel entropy component regression model for industrial system[J]. OPTIMAL CONTROL APPLICATIONS & METHODS,2021:1-16.
APA Sun, Chengyuan,Kang HB,Ma HJ,&Bai, Hua.(2021).A key performance indicator-relevant approach based on kernel entropy component regression model for industrial system.OPTIMAL CONTROL APPLICATIONS & METHODS,1-16.
MLA Sun, Chengyuan,et al."A key performance indicator-relevant approach based on kernel entropy component regression model for industrial system".OPTIMAL CONTROL APPLICATIONS & METHODS (2021):1-16.
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