Addressing Subjectivity in FMEA: A Fuzzy Probabilistic Linguistic Framework With Objective Risk Factor Derivation.
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| Title: | Addressing Subjectivity in FMEA: A Fuzzy Probabilistic Linguistic Framework With Objective Risk Factor Derivation. |
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| Authors: | Sun, Bo1 (AUTHOR), Wang, Lei1 (AUTHOR) 230101019@mails.ccu.edu.cn, Wang, Hao1 (AUTHOR), Ding, Ning1 (AUTHOR), Zhang, Jian1 (AUTHOR), Manolakos, Dimitrios E.1 (AUTHOR) manolako@central.ntua.gr |
| Source: | Modelling & Simulation in Engineering. 6/26/2026, Vol. 2026, p1-18. 18p. |
| Subjects: | Failure mode & effects analysis, TOPSIS method, Risk assessment, Analytic hierarchy process, Statistical weighting |
| Abstract: | As a reliability analysis method for evaluating systems, failure mode and effects analysis (FMEA) is widely used to assess potential failure modes and their impacts on systems. However, FMEA faces limitations: the handling of expert evaluation information and their weights during the assessment and aggregation of risk evaluation information can significantly influence the results. Meanwhile, when ranking failure modes, over‐reliance on experts′ subjective assessments of risk factors (RFs) leads to lower credibility of the outcomes. To address these shortcomings, this paper proposes a novel FMEA method integrating fuzzy probabilistic linguistic term sets (FPLTS) with system‐derived objective data. Unlike existing approaches that primarily focus on subjective expert weighting, this study introduces a quantitative mechanism to derive objective severity (S) values based on the physical topology (series vs. parallel connections) of the system components. First, FPLTS are employed to explicitly model the cognitive uncertainty and hesitation in expert probability assignments using triangular fuzzy sets. Second, a Mahalanobis distance (MD) weighting scheme is utilized to objectively measure expert consensus without assuming prior weight distributions. Crucially, the proposed method bridges the gap between subjective risk perception and physical system reality by using the structural load‐sharing characteristics to correct subjective severity estimates. These elements are seamlessly integrated into a fuzzy analytic hierarchy process (FAHP) and fuzzy TOPSIS framework to robustly rank failure modes. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | As a reliability analysis method for evaluating systems, failure mode and effects analysis (FMEA) is widely used to assess potential failure modes and their impacts on systems. However, FMEA faces limitations: the handling of expert evaluation information and their weights during the assessment and aggregation of risk evaluation information can significantly influence the results. Meanwhile, when ranking failure modes, over‐reliance on experts′ subjective assessments of risk factors (RFs) leads to lower credibility of the outcomes. To address these shortcomings, this paper proposes a novel FMEA method integrating fuzzy probabilistic linguistic term sets (FPLTS) with system‐derived objective data. Unlike existing approaches that primarily focus on subjective expert weighting, this study introduces a quantitative mechanism to derive objective severity (S) values based on the physical topology (series vs. parallel connections) of the system components. First, FPLTS are employed to explicitly model the cognitive uncertainty and hesitation in expert probability assignments using triangular fuzzy sets. Second, a Mahalanobis distance (MD) weighting scheme is utilized to objectively measure expert consensus without assuming prior weight distributions. Crucially, the proposed method bridges the gap between subjective risk perception and physical system reality by using the structural load‐sharing characteristics to correct subjective severity estimates. These elements are seamlessly integrated into a fuzzy analytic hierarchy process (FAHP) and fuzzy TOPSIS framework to robustly rank failure modes. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 16875591 |
| DOI: | 10.1155/mse/7381679 |
