An approach to robust condition monitoring in industrial processes using pythagorean membership grades Cruz Corona, Carlos Alberto Rodríguez Ramos, Adrián Bernal de Lázaro, José M. Llanes Santiago, Orestes Da Silva Neto, Antônio José 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. 2024-02-08T11:43:50Z 2024-02-08T11:43:50Z 2022 journal article RAMOS AR, LÁZARO JMB, CORONA CC, SILVA NETO AJ & LLANES-SANTIAGO O. 2022. An approach to robust condition monitoring in industrial processes using pythagorean membership grades. An Acad Bras Cienc 94: e20200662 https://hdl.handle.net/10481/88725 10.1590/0001-3765202220200662 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Anais da Academia Brasileira de Ciências