| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560767 |
| ISSN: | 1405-5546 |
| Autores: | Rodríguez Ramos, Adrián1 Ortiz, Francisco Javier2 Llanes Santiago, Orestes1 |
| Instituciones: | 1Universidad Tecnológica de La Habana José Antonio Echeverría, Departamento de Automática y Computación, Cuba 2Universidad Politécnica Salesiana, Programa de Maestría en Electrónica y Automatización, Azuay. Ecuador |
| Año: | 2023 |
| Periodo: | Ene-Mar |
| Volumen: | 27 |
| Número: | 1 |
| Paginación: | 223-235 |
| País: | México |
| Idioma: | Inglés |
| Resumen en inglés | The Industry 4.0 paradigm aims to obtain high levels of productivity and efficiency, more competitive final products and compliance with the demanding regulations related to industrial safety. To achieve these objectives, the industrial systems must be equipped with condition monitoring systems for early detection, isolation, and location of faults. The paper presents a proposal for a condition monitoring system characterized by its robustness in presence of noise and missing variables in the measurements. The proposal combines the use of simple and effective imputation algorithms with a fuzzy classification kernel algorithm based on the use of the non-standard Pythagorean fuzzy sets. The proposed scheme was validated using the known DAMADICS test problem with excellent results. |
| Keyword: | Robust condition monitoring, Pythagorean fuzzy sets, Missing information, Noise |
| Texto completo: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
