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

Detalhes bibliográficos
Autor(a) principal: RAMOS,ADRIÁN RODRÍGUEZ
Data de Publicação: 2022
Outros Autores: LÁZARO,JOSÉ M. BERNAL DE, CORONA,CARLOS CRUZ, SILVA NETO,ANTÔNIO J. DA, LLANES-SANTIAGO,ORESTES
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Anais da Academia Brasileira de Ciências (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652022000601707
Resumo: Abstract 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.
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spelling An approach to robust condition monitoring in industrial processes using pythagorean membership gradesRobust diagnostic approachindustrial plantsfuzzy algorithmspythagorean fuzzy setsAbstract 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.Academia Brasileira de Ciências2022-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652022000601707Anais da Academia Brasileira de Ciências v.94 n.4 2022reponame:Anais da Academia Brasileira de Ciências (Online)instname:Academia Brasileira de Ciências (ABC)instacron:ABC10.1590/0001-3765202220200662info:eu-repo/semantics/openAccessRAMOS,ADRIÁN RODRÍGUEZLÁZARO,JOSÉ M. BERNAL DECORONA,CARLOS CRUZSILVA NETO,ANTÔNIO J. DALLANES-SANTIAGO,ORESTESeng2022-12-02T00:00:00Zoai:scielo:S0001-37652022000601707Revistahttp://www.scielo.br/aabchttps://old.scielo.br/oai/scielo-oai.php||aabc@abc.org.br1678-26900001-3765opendoar:2022-12-02T00:00Anais da Academia Brasileira de Ciências (Online) - Academia Brasileira de Ciências (ABC)false
dc.title.none.fl_str_mv An approach to robust condition monitoring in industrial processes using pythagorean membership grades
title An approach to robust condition monitoring in industrial processes using pythagorean membership grades
spellingShingle An approach to robust condition monitoring in industrial processes using pythagorean membership grades
RAMOS,ADRIÁN RODRÍGUEZ
Robust diagnostic approach
industrial plants
fuzzy algorithms
pythagorean fuzzy sets
title_short An approach to robust condition monitoring in industrial processes using pythagorean membership grades
title_full An approach to robust condition monitoring in industrial processes using pythagorean membership grades
title_fullStr An approach to robust condition monitoring in industrial processes using pythagorean membership grades
title_full_unstemmed An approach to robust condition monitoring in industrial processes using pythagorean membership grades
title_sort An approach to robust condition monitoring in industrial processes using pythagorean membership grades
author RAMOS,ADRIÁN RODRÍGUEZ
author_facet RAMOS,ADRIÁN RODRÍGUEZ
LÁZARO,JOSÉ M. BERNAL DE
CORONA,CARLOS CRUZ
SILVA NETO,ANTÔNIO J. DA
LLANES-SANTIAGO,ORESTES
author_role author
author2 LÁZARO,JOSÉ M. BERNAL DE
CORONA,CARLOS CRUZ
SILVA NETO,ANTÔNIO J. DA
LLANES-SANTIAGO,ORESTES
author2_role author
author
author
author
dc.contributor.author.fl_str_mv RAMOS,ADRIÁN RODRÍGUEZ
LÁZARO,JOSÉ M. BERNAL DE
CORONA,CARLOS CRUZ
SILVA NETO,ANTÔNIO J. DA
LLANES-SANTIAGO,ORESTES
dc.subject.por.fl_str_mv Robust diagnostic approach
industrial plants
fuzzy algorithms
pythagorean fuzzy sets
topic Robust diagnostic approach
industrial plants
fuzzy algorithms
pythagorean fuzzy sets
description Abstract 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.
publishDate 2022
dc.date.none.fl_str_mv 2022-01-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652022000601707
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652022000601707
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/0001-3765202220200662
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Academia Brasileira de Ciências
publisher.none.fl_str_mv Academia Brasileira de Ciências
dc.source.none.fl_str_mv Anais da Academia Brasileira de Ciências v.94 n.4 2022
reponame:Anais da Academia Brasileira de Ciências (Online)
instname:Academia Brasileira de Ciências (ABC)
instacron:ABC
instname_str Academia Brasileira de Ciências (ABC)
instacron_str ABC
institution ABC
reponame_str Anais da Academia Brasileira de Ciências (Online)
collection Anais da Academia Brasileira de Ciências (Online)
repository.name.fl_str_mv Anais da Academia Brasileira de Ciências (Online) - Academia Brasileira de Ciências (ABC)
repository.mail.fl_str_mv ||aabc@abc.org.br
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