Non-Singleton Type-3 Fuzzy Approach for Flowmeter Fault Detection: Experimental Study in a Gas Industry

The main contribution of this paper is to develop a new flowmeter fault detection approach based on optimized non-singleton type-3 (NT3) fuzzy logic systems (FLSs). The introduced method is implemented on an experimental gas industry plant. The system is modeled by NT3FLSs, and the faults are detect...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Jing-he Wang, Jafar Tavoosi, Ardashir Mohammadzadeh, Saleh Mobayen, Jihad H. Asad, Wudhichai Assawinchaichote, Mai The Vu, Paweł Skruch
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	learning algorithm fault detection type-3 fuzzy logic non-Gaussian noise correntropy Kalman filter Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/d434c1d0387345ee95e77e0afa48b757
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:d434c1d0387345ee95e77e0afa48b757
record_format	dspace
spelling	oai:doaj.org-article:d434c1d0387345ee95e77e0afa48b7572021-11-11T19:20:05ZNon-Singleton Type-3 Fuzzy Approach for Flowmeter Fault Detection: Experimental Study in a Gas Industry10.3390/s212174191424-8220https://doaj.org/article/d434c1d0387345ee95e77e0afa48b7572021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7419https://doaj.org/toc/1424-8220The main contribution of this paper is to develop a new flowmeter fault detection approach based on optimized non-singleton type-3 (NT3) fuzzy logic systems (FLSs). The introduced method is implemented on an experimental gas industry plant. The system is modeled by NT3FLSs, and the faults are detected by comparison of measured end estimated signals. In this scheme, the detecting performance depends on the estimation and modeling performance. The suggested NT3FLS is used because of the existence of a high level of measurement errors and uncertainties in this problem. The designed NT3FLS with uncertain footprint-of-uncertainty (FOU), fuzzy secondary memberships and adaptive non-singleton fuzzification results in a powerful tool for modeling signals immersed in noise and error. The level of non-singleton fuzzification and membership parameters are tuned by maximum correntropy (MC) unscented Kalman filter (KF), and the rule parameters are learned by correntropy KF (CKF) with fuzzy kernel size. The suggested learning algorithms can handle the non-Gaussian noises that are common in industrial applications. The various types of flowmeters are investigated, and the effect of common faults are examined. It is shown that the suggested approach can detect the various faults with good accuracy in comparison with conventional approaches.Jing-he WangJafar TavoosiArdashir MohammadzadehSaleh MobayenJihad H. AsadWudhichai AssawinchaichoteMai The VuPaweł SkruchMDPI AGarticlelearning algorithmfault detectiontype-3 fuzzy logicnon-Gaussian noisecorrentropy Kalman filterChemical technologyTP1-1185ENSensors, Vol 21, Iss 7419, p 7419 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	learning algorithm fault detection type-3 fuzzy logic non-Gaussian noise correntropy Kalman filter Chemical technology TP1-1185
spellingShingle	learning algorithm fault detection type-3 fuzzy logic non-Gaussian noise correntropy Kalman filter Chemical technology TP1-1185 Jing-he Wang Jafar Tavoosi Ardashir Mohammadzadeh Saleh Mobayen Jihad H. Asad Wudhichai Assawinchaichote Mai The Vu Paweł Skruch Non-Singleton Type-3 Fuzzy Approach for Flowmeter Fault Detection: Experimental Study in a Gas Industry
description	The main contribution of this paper is to develop a new flowmeter fault detection approach based on optimized non-singleton type-3 (NT3) fuzzy logic systems (FLSs). The introduced method is implemented on an experimental gas industry plant. The system is modeled by NT3FLSs, and the faults are detected by comparison of measured end estimated signals. In this scheme, the detecting performance depends on the estimation and modeling performance. The suggested NT3FLS is used because of the existence of a high level of measurement errors and uncertainties in this problem. The designed NT3FLS with uncertain footprint-of-uncertainty (FOU), fuzzy secondary memberships and adaptive non-singleton fuzzification results in a powerful tool for modeling signals immersed in noise and error. The level of non-singleton fuzzification and membership parameters are tuned by maximum correntropy (MC) unscented Kalman filter (KF), and the rule parameters are learned by correntropy KF (CKF) with fuzzy kernel size. The suggested learning algorithms can handle the non-Gaussian noises that are common in industrial applications. The various types of flowmeters are investigated, and the effect of common faults are examined. It is shown that the suggested approach can detect the various faults with good accuracy in comparison with conventional approaches.
format	article
author	Jing-he Wang Jafar Tavoosi Ardashir Mohammadzadeh Saleh Mobayen Jihad H. Asad Wudhichai Assawinchaichote Mai The Vu Paweł Skruch
author_facet	Jing-he Wang Jafar Tavoosi Ardashir Mohammadzadeh Saleh Mobayen Jihad H. Asad Wudhichai Assawinchaichote Mai The Vu Paweł Skruch
author_sort	Jing-he Wang
title	Non-Singleton Type-3 Fuzzy Approach for Flowmeter Fault Detection: Experimental Study in a Gas Industry
title_short	Non-Singleton Type-3 Fuzzy Approach for Flowmeter Fault Detection: Experimental Study in a Gas Industry
title_full	Non-Singleton Type-3 Fuzzy Approach for Flowmeter Fault Detection: Experimental Study in a Gas Industry
title_fullStr	Non-Singleton Type-3 Fuzzy Approach for Flowmeter Fault Detection: Experimental Study in a Gas Industry
title_full_unstemmed	Non-Singleton Type-3 Fuzzy Approach for Flowmeter Fault Detection: Experimental Study in a Gas Industry
title_sort	non-singleton type-3 fuzzy approach for flowmeter fault detection: experimental study in a gas industry
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/d434c1d0387345ee95e77e0afa48b757
work_keys_str_mv	AT jinghewang nonsingletontype3fuzzyapproachforflowmeterfaultdetectionexperimentalstudyinagasindustry AT jafartavoosi nonsingletontype3fuzzyapproachforflowmeterfaultdetectionexperimentalstudyinagasindustry AT ardashirmohammadzadeh nonsingletontype3fuzzyapproachforflowmeterfaultdetectionexperimentalstudyinagasindustry AT salehmobayen nonsingletontype3fuzzyapproachforflowmeterfaultdetectionexperimentalstudyinagasindustry AT jihadhasad nonsingletontype3fuzzyapproachforflowmeterfaultdetectionexperimentalstudyinagasindustry AT wudhichaiassawinchaichote nonsingletontype3fuzzyapproachforflowmeterfaultdetectionexperimentalstudyinagasindustry AT maithevu nonsingletontype3fuzzyapproachforflowmeterfaultdetectionexperimentalstudyinagasindustry AT pawełskruch nonsingletontype3fuzzyapproachforflowmeterfaultdetectionexperimentalstudyinagasindustry
_version_	1718431556128735232

Non-Singleton Type-3 Fuzzy Approach for Flowmeter Fault Detection: Experimental Study in a Gas Industry

Ejemplares similares