Failure Mode and Effect Analysis with a Fuzzy Logic Approach

Abstract

Failure mode and effect analysis (FMEA) is one of the most used techniques in risk management due to its potential to solve multidisciplinary engineering problems. The role of experts is fundamental when developing the FMEA; they identify the failure modes by expressing their opinion based on their experience. A relevant aspect is a way in which the experts evaluate to obtain the indicator of the risk priority number (RPN), which is based on qualitative analysis and a table of criteria where they subjectively and intuitively determine the factor level (severity, occurrence, and detection) for each of the failures. With this, imprecision is present due to the interpretation that each one has regarding the failures. Therefore, this research proposes a fuzzy logic evaluation system with a solid mathematical basis that integrates these conditions of imprecision and uncertainty, thus offering a robust system capable of emulating the evaluation form of experts to support and improve decision making. One of the main contributions of this research is in the defuzzification stage, adjusting the centroid method and treating each set individually. With this, the RPN values approximate to the conventional technique were obtained. Simulations were carried out to test and determine the system’s best structure. The system was validated in a textile company in southern Guanajuato. The results demonstrate that the system reliably represents how experts perform risk assessment.

Risk assessment of organ transplant operation: A fuzzy hybrid MCDM approach based on fuzzy FMEA

“…However, the crucial aspect to consider is the methodology for determining the F-RPN value based on the fuzzy values of O, S, and D. The F-RPN value can be computed by multiplying the membership functions of occurrence, severity and detection according to the following equation [ 72 ]. In the case of S being a linguistic variable, its triangular fuzzy number can be described in the following manner in Eq ( 7 ): …”

REFS - A Risk Evaluation Framework on Supply Chain

“…A multitude of linguistic variables influence the number of membership functions associated with a given variable. Typically, Fuzzy FMEA utilizes three to seven linguistic variables ( [34,35]). It is possible to incorporate additional variables; however, in the given context, the rule base became exceedingly intricate.…”

Application of Mamdani Fuzzy Inference System in Poultry Weight Estimation

“…By integrating expert knowledge and experience, FL systems enhance the accuracy of weight estimation, even when dealing with incomplete or uncertain data. This ability to account for uncertainty and vagueness in the data makes FL a valuable tool for poultry weight estimation, allowing for more reliable results in practical applications [15].…”

“…Literature Review, Research Questions, and Contributions of the Study AI techniques for poultry management have gained popularity worldwide due to their ability to accurately model complex relations among various factors [15][16][17][18][19]. These techniques can handle imprecise and uncertain data and adapt to changing conditions, making them a powerful tool in poultry production [19][20][21].…”

“…However, humans can work with vague, imprecise, and linguistic definitions commonly observed in the real world. Therefore, the inclusion of FL enables handling such complex, uncertain, and imprecise information and can improve the accuracy and reliability of decision-making processes [15,[23][24][25].…”