Abstract
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.
Original language | English |
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Pages (from-to) | 223-235 |
Number of pages | 13 |
Journal | Computacion y Sistemas |
Volume | 27 |
Issue number | 1 |
DOIs | |
State | Published - 2023 |
Bibliographical note
Funding Information:Authors Adrián Rodríguez-Ramos and Orestes Llanes-Santiago acknowledge the financial support provided by the International Funds and Projects Management Office (OGFPI) of the Ministry of Science, Technology and Environment (CITMA) of Cuba for the national project with code PN223LH004-023.
Publisher Copyright:
© 2023 Instituto Politecnico Nacional. All rights reserved.
Keywords
- missing information
- noise
- Pythagorean fuzzy sets
- Robust condition monitoring