Non-singleton type-3 fuzzy approach for flowmeter fault detection: Experimental study in a gas industry

Journal article

Authors/Editors

WUDHICHAI ASSAWINCHAICHOTE

Strategic Research Themes

Other (Strategic Research Themes)

Publication Details

Author list: Wang J.-H., Tavoosi J., Mohammadzadeh A., Mobayen S., Asad J.H., Assawinchaichote W., Vu M.T., Skruch P.

Publisher: MDPI

Publication year: 2021

Volume number: 21

Issue number: 21

ISSN: 1424-8220

eISSN: 1424-8220

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118477451&doi=10.3390%2fs21217419&partnerID=40&md5=f7b48510d311f89ebba0ffb400d182c9

Languages: English-Great Britain (EN-GB)

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Abstract

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. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

Correntropy Kalman filter, Non-Gaussian noise, Type-3 fuzzy logic