An approach to robust condition monitoring in industrial processes using pythagorean membership grades

In this paper, a robust approach to improve the performance of a condition monitoring process in industrial plants by using Pythagorean membership grades is presented. The FCM algorithm is modified by using Pythagorean fuzzy sets, to obtain a new variant of it called Pythagorean Fuzzy C-Means (PyFCM...

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Published inAnais da Academia Brasileira de Ciências Vol. 94; no. 4; p. e20200662
Main Authors Ramos, Adrián Rodríguez, Lázaro, José M Bernal DE, Corona, Carlos Cruz, Silva Neto, Antônio J DA, Llanes-Santiago, Orestes
Format Journal Article
LanguageEnglish
Portuguese
Published Brazil Academia Brasileira de Ciências 01.01.2022
Subjects
fuzzy algorithms
industrial plants
MULTIDISCIPLINARY SCIENCES
pythagorean fuzzy sets
Robust diagnostic approach
Robust diagnostic approach
fuzzy algorithms
industrial plants
pythagorean fuzzy sets
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Summary:In this paper, a robust approach to improve the performance of a condition monitoring process in industrial plants by using Pythagorean membership grades is presented. The FCM algorithm is modified by using Pythagorean fuzzy sets, to obtain a new variant of it called Pythagorean Fuzzy C-Means (PyFCM). In addition, a kernel version of PyFCM (KPyFCM) is obtained in order to achieve greater separability among classes, and reduce classification errors. The approach proposed is validated using experimental datasets and the Tennessee Eastman (TE) process benchmark. The results are compared with the results obtained with other algorithms that use standard and non-standard membership grades. The highest performance obtained by the approach proposed indicate its feasibility.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:0001-3765
1678-2690
1678-2690
DOI:10.1590/0001-3765202220200662