Version 1
: Received: 25 January 2023 / Approved: 26 January 2023 / Online: 26 January 2023 (16:10:15 CET)
How to cite:
Zúñiga, A. A.; Fernandes, J. F. P.; Branco, P. J. C. Fuzzy-Based Failure Modes, Effects and Criticality Analysis Applied to Cyber-Power Grids. Preprints2023, 2023010482. https://doi.org/10.20944/preprints202301.0482.v1
Zúñiga, A. A.; Fernandes, J. F. P.; Branco, P. J. C. Fuzzy-Based Failure Modes, Effects and Criticality Analysis Applied to Cyber-Power Grids. Preprints 2023, 2023010482. https://doi.org/10.20944/preprints202301.0482.v1
Zúñiga, A. A.; Fernandes, J. F. P.; Branco, P. J. C. Fuzzy-Based Failure Modes, Effects and Criticality Analysis Applied to Cyber-Power Grids. Preprints2023, 2023010482. https://doi.org/10.20944/preprints202301.0482.v1
APA Style
Zúñiga, A. A., Fernandes, J. F. P., & Branco, P. J. C. (2023). Fuzzy-Based Failure Modes, Effects and Criticality Analysis Applied to Cyber-Power Grids. Preprints. https://doi.org/10.20944/preprints202301.0482.v1
Chicago/Turabian Style
Zúñiga, A. A., João Filipe Pereira Fernandes and Paulo J. C. Branco. 2023 "Fuzzy-Based Failure Modes, Effects and Criticality Analysis Applied to Cyber-Power Grids" Preprints. https://doi.org/10.20944/preprints202301.0482.v1
Abstract
In this paper, we introduce the application of Type-I fuzzy inference systems (FIS) as an alternative to improve the prioritization in the FMECA analysis applied in cyber-power grids. Classical FMECA assesses the risk level through the Risk Priority Number (RPN). The multiplication between three integer numbers computes this, called risk factors, representing the severity, occurrence, and detectability of each failure mode and are defined by a team of experts. The RPN does not consider any relative importance between the risk factors and may not necessarily represent the real risk perception of the FMECA team members, usually expressed by natural language; this is the main FMECA shortcoming criticized in the literature. Our approach considers fuzzy variables defined by FMECA experts to represent the uncertainty associated with the human language and a rule base consisting of 125 fuzzy rules to represent the risk perception of the experts. To test our approach, we select a cyber-power grid previously analyzed by the authors using the classical FMECA. The results reveal our proposed fuzzy approach as promissory to represent the uncertainty associated with expert knowledge and to perform an accurate prioritization of failure modes in the context of electrical power systems.
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
