Fuzzy-Based Failure Modes, Effects and Criticality Analysis Applied to Cyber-Power Grids

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.

Reliability analysis of subway sliding plug doors based on improved FMECA and Weibull distribution

“…It combined them with an artificial network to predict the monitored variables with an error of less than 10%, proving the effectiveness of the proposed method. In the literature [46], the Type-I fuzzy inference system is used as an alternative method to improve the failure modes' risk level computation in the classic FMECA analysis and applied to the networked power grids. A method of optimizing the process parameters of mechanical parts manufacturing technology based on the FMECA method is proposed in the literature [27].…”

Fuzzy Logic in Risk Assessment of Production Machines Failure in Forming and Assembly Processes

“…These are i.e. using fuzzy logic in FMECA to analyse the reliability of cyber-power grid systems [3], a comparison of traditional and fuzzy FMEA in identifying risks in tests and calibration laboratories [4], in pharmaceutical production process [5], logistic systems failures during COVID-19 pandemic [6], problems with drought and other risk factors affecting the functioning of the agricultural industry [7], hazards that are connected with the underground coal mines [8], the IFF system used in military helicopters [9], collecting data from people by smartphones [10] and many more. Thus, fuzzy FMEA has many applications, due to the universal character of the FMEA method itself.…”

Fuzzy Failure Modes Effect and Criticality Analysis of the Procurement Process of Artificial Intelligent Systems/Services

“…Incorporating the criticality analysis in Fuzzy FMEA, the Fuzzy FMECA was developed as an advanced method for ranking the risks. Numerous fields, including engineering [28], manufacturing [29], healthcare [30], and more recently, new technologies like artificial intelligence [31], have found use for fuzzy FMECA. When working with complicated systems where it might be challenging to exactly quantify risk variables, fuzzy FMECA is particularly helpful.…”