Fault detection algorithm of industrial process based on DW-ICA-SVM

In order to effectively improve the fault detection and monitoring performance of support vector machine (SVM) algorithm,a new fault detection algorithm of industrial process based on DW-ICA-SVM was proposed.Firstly,the training data was normalized.The independent component analysis (ICA) was used t...

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Autores principales:	Jinyu GUO, Tao LI, Yuan LI
Formato:	article
Lenguaje:	ZH
Publicado:	Hebei University of Science and Technology 2021
Materias:	other disciplines of automatic control technology; fault detection; durbin-watson criterion; independent component analysis; support vector machine Technology T
Acceso en línea:	https://doaj.org/article/e9a8907d4ee34556971c8cdf4c8fd494
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spelling	oai:doaj.org-article:e9a8907d4ee34556971c8cdf4c8fd4942021-11-23T07:08:58ZFault detection algorithm of industrial process based on DW-ICA-SVM1008-154210.7535/hbkd.2021yx04007https://doaj.org/article/e9a8907d4ee34556971c8cdf4c8fd4942021-08-01T00:00:00Zhttp://xuebao.hebust.edu.cn/hbkjdx/ch/reader/create_pdf.aspx?file_no=b202104007&flag=1&journal_https://doaj.org/toc/1008-1542In order to effectively improve the fault detection and monitoring performance of support vector machine (SVM) algorithm,a new fault detection algorithm of industrial process based on DW-ICA-SVM was proposed.Firstly,the training data was normalized.The independent component analysis (ICA) was used to obtain the independent component matrix of the data and extract the hidden non-Gaussian information.Then the Durbin-Watson (DW) criterion was used to calculate the DW values of the independent components (ICs).The DW method was used to effectively extract important noise information and select the important ICs.The ICs containing important information were used as the input of the SVM model to obtain the discriminant classification function.The ICs of test data were input to the model for fault detection and monitoring.Finally,the method was applied to the nonlinear numerical example and the Tennessee-Eastman industrial process,and compared with PCA,LPP,ICA,SVM and ICA-SVM methods.The results show that the proposed method reduces the autocorrelation among samples and effectively improves the fault detection rate.The proposed method strengthens the extraction and recognition of hidden non-Gaussian information to a certain extent,and provides a reference for improving the performance of SVM algorithm in fault detection of industrial process.Jinyu GUOTao LIYuan LIHebei University of Science and Technologyarticleother disciplines of automatic control technology; fault detection; durbin-watson criterion; independent component analysis; support vector machineTechnologyTZHJournal of Hebei University of Science and Technology, Vol 42, Iss 4, Pp 369-379 (2021)
institution	DOAJ
collection	DOAJ
language	ZH
topic	other disciplines of automatic control technology; fault detection; durbin-watson criterion; independent component analysis; support vector machine Technology T
spellingShingle	other disciplines of automatic control technology; fault detection; durbin-watson criterion; independent component analysis; support vector machine Technology T Jinyu GUO Tao LI Yuan LI Fault detection algorithm of industrial process based on DW-ICA-SVM
description	In order to effectively improve the fault detection and monitoring performance of support vector machine (SVM) algorithm,a new fault detection algorithm of industrial process based on DW-ICA-SVM was proposed.Firstly,the training data was normalized.The independent component analysis (ICA) was used to obtain the independent component matrix of the data and extract the hidden non-Gaussian information.Then the Durbin-Watson (DW) criterion was used to calculate the DW values of the independent components (ICs).The DW method was used to effectively extract important noise information and select the important ICs.The ICs containing important information were used as the input of the SVM model to obtain the discriminant classification function.The ICs of test data were input to the model for fault detection and monitoring.Finally,the method was applied to the nonlinear numerical example and the Tennessee-Eastman industrial process,and compared with PCA,LPP,ICA,SVM and ICA-SVM methods.The results show that the proposed method reduces the autocorrelation among samples and effectively improves the fault detection rate.The proposed method strengthens the extraction and recognition of hidden non-Gaussian information to a certain extent,and provides a reference for improving the performance of SVM algorithm in fault detection of industrial process.
format	article
author	Jinyu GUO Tao LI Yuan LI
author_facet	Jinyu GUO Tao LI Yuan LI
author_sort	Jinyu GUO
title	Fault detection algorithm of industrial process based on DW-ICA-SVM
title_short	Fault detection algorithm of industrial process based on DW-ICA-SVM
title_full	Fault detection algorithm of industrial process based on DW-ICA-SVM
title_fullStr	Fault detection algorithm of industrial process based on DW-ICA-SVM
title_full_unstemmed	Fault detection algorithm of industrial process based on DW-ICA-SVM
title_sort	fault detection algorithm of industrial process based on dw-ica-svm
publisher	Hebei University of Science and Technology
publishDate	2021
url	https://doaj.org/article/e9a8907d4ee34556971c8cdf4c8fd494
work_keys_str_mv	AT jinyuguo faultdetectionalgorithmofindustrialprocessbasedondwicasvm AT taoli faultdetectionalgorithmofindustrialprocessbasedondwicasvm AT yuanli faultdetectionalgorithmofindustrialprocessbasedondwicasvm
_version_	1718416857819512832

Fault detection algorithm of industrial process based on DW-ICA-SVM

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