Fuzzy Failure Modes and Effects Analysis Using Summative Defuzzification Methods

Pokorádi, László; Koçak, Sinan; Tóth-Laufer, Edit

doi:10.12700/aph.18.9.2021.9.7

Cited by 14 publications

(5 citation statements)

References 9 publications

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“…The aggregated fuzzy sets reflecting the expert evaluations should first be determined using the CoA method by examining only the overlapping areas. Then, the produced fuzzy sets should be combined using the COG method, which measures overlapped areas of sub-conclusions multiple times (Figure 6) [18].…”

Section: Fuzzy Fmea With Summative Defuzzification Methodsmentioning

confidence: 99%

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FMEA in Smartphones: A Fuzzy Approach

et al. 2022

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Smartphones are attracting increasing interest due to how they are revolutionizing our lives. On the other hand, hardware and software failures that occur in them are continually present. This work aims to investigate these failures in a typical smartphone by collecting data from a class of people. Concerns have been raised that call into question the efficiency of applied methods for identifying and prioritizing the potential defects. The widely used hybridized engineering method, Fuzzy Failure Mode and Effect Analysis (F-FMEA), is an excellent approach to solving these problems. The F-FMEA method was applied to prioritize the potential failures based on their Severity (S), expected Occurrence (O), and the likelihood of Detectability (D). After collecting failure data from different users on a selected smartphone, two well-known defuzzification methods facing the Risk Priority Number (RPN) in F-FMEA were applied. Despite this interest, to the best of our knowledge, no one has studied smartphone failures with a technique that combines the results of different fuzzy applications. Thus, to combine the results of the derived fuzzy subsystems for the average value, we suggest a summative defuzzification method. Our findings indicate that F-FMEA with a summative defuzzification procedure is a clear improvement on the F-FMEA method. Even though the summation method modifies close results of the defuzzification one, it was shown that it provides more accurate results.

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Section: Fuzzy Fmea With Summative Defuzzification Methodsmentioning

confidence: 99%

“…Pokorádi and other authors have focused on risk assessment methods and have contributed many investigations in the literature [16][17][18]. They have proposed several Summative Defuzzification (SMDF) methods such as centroid, bisector, and summarized weighted mean of maxima.…”

Section: Introductionmentioning

confidence: 99%

FMEA in Smartphones: A Fuzzy Approach

et al. 2022

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“…First, we define a discourse universe for each input and categorize it into relevant classes. The membership function is assigned to each parameter's actual value in the first step, which is referred to as "fuzzification," turning the input data into a fuzzy set [18].…”

Section: Fuzzificationmentioning

confidence: 99%

“…For this purpose, Akram et al [11] gave a new approach based on bipolar fuzzy PROMETHEE to choose green suppliers. Li et al [18] suggested an enhanced FMEA model by combining FMEA, the AHP, and the TOPSIS method to find a more reasonable failure mode level. Rezaee et al [12] defined and controlled risks in stone manufacture utilizing FMEA and DEA methods instead of the RPN concept to rank the criticality.…”

Section: Introductionmentioning

confidence: 99%

A Neuro-Fuzzy Risk Prediction Methodology in the Automotive Part Industry

Chakhrit,

Djelamda,

Bougofa

et al. 2024

Preprint

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Failure mode and effects analysis (FMEA) is a systematic and structured method employed across diverse industries to proactively identify and evaluate potential failure modes. In a traditional FMEA, for all failure modes, three criticality parameters: severity, detection, and frequency, are assessed to evaluate criticality. Nevertheless, it frequently has certain flaws. Therefore, in this work, a fuzzy risk proposed model is used to improve the use of the FMEA methodology. The new model uses a fuzzy inference technique in place of the conventional criticality calculation. Fuzzy logic technique is used where the various factors are given as members of a fuzzy set fuzzified by employing adequate membership functions to evaluate the risk and then ranking failure modes and preferring measures to control the risks of undesired events. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is suggested as a dynamic, intelligently proposed model to improve and validate the results acquired by the fuzzy inference system and effectively predict the criticality evaluation of failure modes. Finally, an automotive parts industry case is presented to show the potential of the suggested model. This analysis offers a different ranking of failure modes and improves the decision-making by providing a “preventive –corrective plan. A comparison with existing approaches is presented to demonstrate the efficiency of the suggested approach.

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“…The equation defines the crisp value of the summative Centroid defuzzification method (BSCoG) following [9],…”

Section: Figure 5 Summative Defuzzification Process Based On Fuzzy In...mentioning

confidence: 99%