An approach to robust condition monitoring in industrial processes using pythagorean membership grades
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-Mea...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Academia Brasileira de Ciências
2022-12-01
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| Series: | Anais da Academia Brasileira de Ciências |
| Subjects: | |
| Online Access: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652022000601707&tlng=en |
| _version_ | 1811185999893495808 |
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| author | ADRIÁN RODRÍGUEZ RAMOS JOSÉ M. BERNAL DE LÁZARO CARLOS CRUZ CORONA ANTÔNIO J. DA SILVA NETO ORESTES LLANES-SANTIAGO |
| author_facet | ADRIÁN RODRÍGUEZ RAMOS JOSÉ M. BERNAL DE LÁZARO CARLOS CRUZ CORONA ANTÔNIO J. DA SILVA NETO ORESTES LLANES-SANTIAGO |
| author_sort | ADRIÁN RODRÍGUEZ RAMOS |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-11T13:39:03Z |
| format | Article |
| id | doaj.art-abd9824890af4fb3b86412f551beec4f |
| institution | Directory Open Access Journal |
| issn | 1678-2690 |
| language | English |
| last_indexed | 2024-04-11T13:39:03Z |
| publishDate | 2022-12-01 |
| publisher | Academia Brasileira de Ciências |
| record_format | Article |
| series | Anais da Academia Brasileira de Ciências |
| spelling | doaj.art-abd9824890af4fb3b86412f551beec4f2022-12-22T04:21:21ZengAcademia Brasileira de CiênciasAnais da Academia Brasileira de Ciências1678-26902022-12-0194410.1590/0001-3765202220200662An approach to robust condition monitoring in industrial processes using pythagorean membership gradesADRIÁN RODRÍGUEZ RAMOShttps://orcid.org/0000-0002-0240-7491JOSÉ M. BERNAL DE LÁZAROhttps://orcid.org/0000-0002-2797-0205CARLOS CRUZ CORONAhttps://orcid.org/0000-0003-2072-4949ANTÔNIO J. DA SILVA NETOhttps://orcid.org/0000-0002-9616-6093ORESTES LLANES-SANTIAGOhttps://orcid.org/0000-0002-6864-9629Abstract 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.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652022000601707&tlng=enRobust diagnostic approachindustrial plantsfuzzy algorithmspythagorean fuzzy sets |
| spellingShingle | ADRIÁN RODRÍGUEZ RAMOS JOSÉ M. BERNAL DE LÁZARO CARLOS CRUZ CORONA ANTÔNIO J. DA SILVA NETO ORESTES LLANES-SANTIAGO An approach to robust condition monitoring in industrial processes using pythagorean membership grades Anais da Academia Brasileira de Ciências Robust diagnostic approach industrial plants fuzzy algorithms pythagorean fuzzy sets |
| title | 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_short | An approach to robust condition monitoring in industrial processes using pythagorean membership grades |
| title_sort | approach to robust condition monitoring in industrial processes using pythagorean membership grades |
| topic | Robust diagnostic approach industrial plants fuzzy algorithms pythagorean fuzzy sets |
| url | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652022000601707&tlng=en |
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