Fault detection of industrial process based on ensemble kernel entropy component analysis algorithm

To solve the problem caused by kernel entropy component analysis (KECA) for selecting the same kernel parameters for different faults,a fault detection of industrial process based on ensemble kernel entropy component analysis (EKECA) was proposed.Firstly,a series of kernel functions with different w...

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Autores principales:	Jinyu GUO, Wenjun ZHAO, Yuan LI
Formato:	article
Lenguaje:	ZH
Publicado:	Hebei University of Science and Technology 2021
Materias:	other disciplines of automatic control technology; kernel entropy component analysis; gaussian kernel function; bayesian decision; ensemble learning method Technology T
Acceso en línea:	https://doaj.org/article/5d81e46cb4ae45be968e1cc56ba0008d
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spelling	oai:doaj.org-article:5d81e46cb4ae45be968e1cc56ba0008d2021-11-23T07:09:07ZFault detection of industrial process based on ensemble kernel entropy component analysis algorithm1008-154210.7535/hbkd.2021yx05006https://doaj.org/article/5d81e46cb4ae45be968e1cc56ba0008d2021-10-01T00:00:00Zhttp://xuebao.hebust.edu.cn/hbkjdx/ch/reader/create_pdf.aspx?file_no=b202105006&flag=1&journal_https://doaj.org/toc/1008-1542To solve the problem caused by kernel entropy component analysis (KECA) for selecting the same kernel parameters for different faults,a fault detection of industrial process based on ensemble kernel entropy component analysis (EKECA) was proposed.Firstly,a series of kernel functions with different width parameters were selected to project the nonlinear data into the kernel feature space.The eigenvalues and eigenvectors with large contribution to Rényi entropy were selected to obtain the transformed score matrix.The multiple KECAsubmodels were established.Secondly,the test data were projected onto each KECA submodel.The statistics of each KECA submodel were calculated to obtain the detection results.Finally,the detection results of each KECA submodel were turned into probability by Bayesian decision.The unified statistics were calculated by ensemble learning strategy and judged whether it exceeds the control limit.The algorithm was applied to a numerical example and the TE process.The simulation results show that the proposed algorithm can effectively improve the fault detection rate and reduce the false alarm rate compared with traditional EKPCA,KECA and other algorithms.This method solves the problem of selecting kernel parameters for different faults in the traditional KECA algorithm and provides a reference for improving the performance of KECA algorithm in fault detection of nonlinear industrial processes.[HQ]Jinyu GUOWenjun ZHAOYuan LIHebei University of Science and Technologyarticleother disciplines of automatic control technology; kernel entropy component analysis; gaussian kernel function; bayesian decision; ensemble learning methodTechnologyTZHJournal of Hebei University of Science and Technology, Vol 42, Iss 5, Pp 481-490 (2021)
institution	DOAJ
collection	DOAJ
language	ZH
topic	other disciplines of automatic control technology; kernel entropy component analysis; gaussian kernel function; bayesian decision; ensemble learning method Technology T
spellingShingle	other disciplines of automatic control technology; kernel entropy component analysis; gaussian kernel function; bayesian decision; ensemble learning method Technology T Jinyu GUO Wenjun ZHAO Yuan LI Fault detection of industrial process based on ensemble kernel entropy component analysis algorithm
description	To solve the problem caused by kernel entropy component analysis (KECA) for selecting the same kernel parameters for different faults,a fault detection of industrial process based on ensemble kernel entropy component analysis (EKECA) was proposed.Firstly,a series of kernel functions with different width parameters were selected to project the nonlinear data into the kernel feature space.The eigenvalues and eigenvectors with large contribution to Rényi entropy were selected to obtain the transformed score matrix.The multiple KECAsubmodels were established.Secondly,the test data were projected onto each KECA submodel.The statistics of each KECA submodel were calculated to obtain the detection results.Finally,the detection results of each KECA submodel were turned into probability by Bayesian decision.The unified statistics were calculated by ensemble learning strategy and judged whether it exceeds the control limit.The algorithm was applied to a numerical example and the TE process.The simulation results show that the proposed algorithm can effectively improve the fault detection rate and reduce the false alarm rate compared with traditional EKPCA,KECA and other algorithms.This method solves the problem of selecting kernel parameters for different faults in the traditional KECA algorithm and provides a reference for improving the performance of KECA algorithm in fault detection of nonlinear industrial processes.[HQ]
format	article
author	Jinyu GUO Wenjun ZHAO Yuan LI
author_facet	Jinyu GUO Wenjun ZHAO Yuan LI
author_sort	Jinyu GUO
title	Fault detection of industrial process based on ensemble kernel entropy component analysis algorithm
title_short	Fault detection of industrial process based on ensemble kernel entropy component analysis algorithm
title_full	Fault detection of industrial process based on ensemble kernel entropy component analysis algorithm
title_fullStr	Fault detection of industrial process based on ensemble kernel entropy component analysis algorithm
title_full_unstemmed	Fault detection of industrial process based on ensemble kernel entropy component analysis algorithm
title_sort	fault detection of industrial process based on ensemble kernel entropy component analysis algorithm
publisher	Hebei University of Science and Technology
publishDate	2021
url	https://doaj.org/article/5d81e46cb4ae45be968e1cc56ba0008d
work_keys_str_mv	AT jinyuguo faultdetectionofindustrialprocessbasedonensemblekernelentropycomponentanalysisalgorithm AT wenjunzhao faultdetectionofindustrialprocessbasedonensemblekernelentropycomponentanalysisalgorithm AT yuanli faultdetectionofindustrialprocessbasedonensemblekernelentropycomponentanalysisalgorithm
_version_	1718416828776054784

Fault detection of industrial process based on ensemble kernel entropy component analysis algorithm

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