Fault Detection and Identification Based on Explicit Polynomial Mapping and Combined Statistic in Nonlinear Dynamic Processes

Single traditional multivariate statistical monitoring methods, such as principal component analysis (PCA) and canonical variate analysis (CVA), are less effective in nonlinear dynamic processes. Monitoring approaches based on radial basis kernel function have been intensively applied. However, an i...

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Autores principales:	Liangliang Shang, Kexin Shi, Chen Ma, Aibing Qiu, Liang Hua
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Canonical variate combined statistic explicit polynomial mapping fault detection fault identification principal component analysis Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/5608a1053a7f46e3abed2bf0875bacff
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spelling	oai:doaj.org-article:5608a1053a7f46e3abed2bf0875bacff2021-11-18T00:07:47ZFault Detection and Identification Based on Explicit Polynomial Mapping and Combined Statistic in Nonlinear Dynamic Processes2169-353610.1109/ACCESS.2021.3124948https://doaj.org/article/5608a1053a7f46e3abed2bf0875bacff2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9598819/https://doaj.org/toc/2169-3536Single traditional multivariate statistical monitoring methods, such as principal component analysis (PCA) and canonical variate analysis (CVA), are less effective in nonlinear dynamic processes. Monitoring approaches based on radial basis kernel function have been intensively applied. However, an infinite dimension nonlinear mapping is redundant and inefficient. To improve the efficiency of traditional methods and consider the nonlinearity and dynamics simultaneously, this paper proposes canonical variate nonlinear principal component analysis (CV-NPCA) based on explicit polynomial mapping and combined statistic for detecting and identifying faults in nonlinear dynamic processes. There are two main contributions of the proposed method. First, explicit second-order polynomial mapping is introduced to combine CVA with PCA to simultaneously decrease the adverse effects of nonlinearity and dynamics. Second, the <inline-formula> <tex-math notation="LaTeX">$Q_{c}$ </tex-math></inline-formula> statistic combining residual vectors with residual components is proposed, and a two-dimensional (2D) contribution plot and the variable with the largest contribution based on the <inline-formula> <tex-math notation="LaTeX">$Q_{c}$ </tex-math></inline-formula> statistic are given for fault identification in the simulation study. Compared with the results of PCA, CVA, kernel principal component analysis (KPCA), nonlinear dynamic principal component analysis (NDPCA) and kernel entropy component analysis (KECA), the proposed method not only has relatively higher fault detection rates and identification rates but also has lower false alarm rates in the numerical simulation process and the benchmark Tennessee Eastman process.Liangliang ShangKexin ShiChen MaAibing QiuLiang HuaIEEEarticleCanonical variatecombined statisticexplicit polynomial mappingfault detectionfault identificationprincipal component analysisElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 149050-149066 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Canonical variate combined statistic explicit polynomial mapping fault detection fault identification principal component analysis Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Canonical variate combined statistic explicit polynomial mapping fault detection fault identification principal component analysis Electrical engineering. Electronics. Nuclear engineering TK1-9971 Liangliang Shang Kexin Shi Chen Ma Aibing Qiu Liang Hua Fault Detection and Identification Based on Explicit Polynomial Mapping and Combined Statistic in Nonlinear Dynamic Processes
description	Single traditional multivariate statistical monitoring methods, such as principal component analysis (PCA) and canonical variate analysis (CVA), are less effective in nonlinear dynamic processes. Monitoring approaches based on radial basis kernel function have been intensively applied. However, an infinite dimension nonlinear mapping is redundant and inefficient. To improve the efficiency of traditional methods and consider the nonlinearity and dynamics simultaneously, this paper proposes canonical variate nonlinear principal component analysis (CV-NPCA) based on explicit polynomial mapping and combined statistic for detecting and identifying faults in nonlinear dynamic processes. There are two main contributions of the proposed method. First, explicit second-order polynomial mapping is introduced to combine CVA with PCA to simultaneously decrease the adverse effects of nonlinearity and dynamics. Second, the <inline-formula> <tex-math notation="LaTeX">$Q_{c}$ </tex-math></inline-formula> statistic combining residual vectors with residual components is proposed, and a two-dimensional (2D) contribution plot and the variable with the largest contribution based on the <inline-formula> <tex-math notation="LaTeX">$Q_{c}$ </tex-math></inline-formula> statistic are given for fault identification in the simulation study. Compared with the results of PCA, CVA, kernel principal component analysis (KPCA), nonlinear dynamic principal component analysis (NDPCA) and kernel entropy component analysis (KECA), the proposed method not only has relatively higher fault detection rates and identification rates but also has lower false alarm rates in the numerical simulation process and the benchmark Tennessee Eastman process.
format	article
author	Liangliang Shang Kexin Shi Chen Ma Aibing Qiu Liang Hua
author_facet	Liangliang Shang Kexin Shi Chen Ma Aibing Qiu Liang Hua
author_sort	Liangliang Shang
title	Fault Detection and Identification Based on Explicit Polynomial Mapping and Combined Statistic in Nonlinear Dynamic Processes
title_short	Fault Detection and Identification Based on Explicit Polynomial Mapping and Combined Statistic in Nonlinear Dynamic Processes
title_full	Fault Detection and Identification Based on Explicit Polynomial Mapping and Combined Statistic in Nonlinear Dynamic Processes
title_fullStr	Fault Detection and Identification Based on Explicit Polynomial Mapping and Combined Statistic in Nonlinear Dynamic Processes
title_full_unstemmed	Fault Detection and Identification Based on Explicit Polynomial Mapping and Combined Statistic in Nonlinear Dynamic Processes
title_sort	fault detection and identification based on explicit polynomial mapping and combined statistic in nonlinear dynamic processes
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/5608a1053a7f46e3abed2bf0875bacff
work_keys_str_mv	AT liangliangshang faultdetectionandidentificationbasedonexplicitpolynomialmappingandcombinedstatisticinnonlineardynamicprocesses AT kexinshi faultdetectionandidentificationbasedonexplicitpolynomialmappingandcombinedstatisticinnonlineardynamicprocesses AT chenma faultdetectionandidentificationbasedonexplicitpolynomialmappingandcombinedstatisticinnonlineardynamicprocesses AT aibingqiu faultdetectionandidentificationbasedonexplicitpolynomialmappingandcombinedstatisticinnonlineardynamicprocesses AT lianghua faultdetectionandidentificationbasedonexplicitpolynomialmappingandcombinedstatisticinnonlineardynamicprocesses
_version_	1718425226084089856

Fault Detection and Identification Based on Explicit Polynomial Mapping and Combined Statistic in Nonlinear Dynamic Processes

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