Fuzzy Failure Modes and Effects Analysis Methods

Failure Modes and Effects Analysis (FMEA) has long served as a systematic approach to identify, assess and mitigate potential failures in systems, processes and products. Traditional FMEA quantifies risk via a Risk Priority Number (RPN), multiplying severity, occurrence and detectability scores. However, the assignment of crisp numerical values often struggles to capture expert uncertainty and linguistic assessments. Fuzzy FMEA extends the classical framework by incorporating fuzzy set theory to model the imprecision inherent in expert judgements, allowing severity, occurrence and detection ratings to be expressed with membership functions or linguistic variables. Over the past decade, researchers have further enhanced fuzzy FMEA by integrating multi‐attribute decision-making techniques (such as TOPSIS, Best–Worst Method and COPRAS), evidence theory and Bayesian networks to determine more robust weightings, account for interdependencies among failure modes and fuse information from multiple sources. Extensions also include hybridisation with Fault Tree Analysis to derive importance measures for failure factors, and the use of rough sets and D numbers to manage non‐exclusive evaluations. These developments have improved the reliability of risk prioritisation, offered clearer guidance for corrective actions and broadened the applicability of FMEA to complex and uncertain environments, from aerospace components to advanced manufacturing lines and agricultural‐project risk management.

Research from Nature Portfolio

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Research from all publishers

Recent work in high–impact engineering journals has demonstrated the versatility of fuzzy FMEA hybrids. A 2025 study introduced a methodology that combines functional and dysfunctional system analyses with fuzzy FMEA to assess detection and monitoring techniques in advanced manufacturing systems. By integrating component-, machine- and line-level assessments, the approach delivers targeted directives that balance monitoring effectiveness with environmental sustainability. A holistic FMEA model published in 2021 leverages a fuzzy Best–Worst Method to weight severity, occurrence and detection parameters, and embeds a fuzzy Bayesian network to capture conditional dependencies among failure modes. This framework enables experts to express judgements as trapezoidal fuzzy numbers, producing occurrence probabilities that reflect both uncertainty and causal structure. An earlier work from 2019 proposed an integrated Fault Tree Analysis–FMEA model in which minimal cut sets are weighted by importance measures and used to adjust traditional RPN values. Applied to subsea blowout preventers, the hybrid approach revealed shifts in risk rankings and offered a deeper understanding of failure interactions under extreme conditions.

Fuzzy Failure Modes and Effects Analysis Methods publication trend

The graph below shows the total number of articles in fuzzy failure modes and effects analysis methods across all publications each year (not limited to Nature Index journals).

Technical terms

Failure Modes and Effects Analysis (FMEA): A systematic method to identify potential failure modes within a system, assess their effects and prioritise corrective actions based on risk metrics.

Fuzzy Set: A mathematical framework in which elements have degrees of membership, used to model vagueness in expert assessments.

Risk Priority Number (RPN): A metric in FMEA obtained by multiplying severity, occurrence and detectability ratings to rank failure modes by risk.

Best–Worst Method (BWM): A multi-criteria decision-making technique in which experts identify the most and least important criteria and derive weights through pairwise comparisons.

TOPSIS: Technique for Order Preference by Similarity to Ideal Solution, a method that ranks alternatives by their distance from ideal and anti-ideal solutions.

Bayesian Network: A probabilistic graphical model representing variables and their conditional dependencies, used to capture the interrelation of failure events.

Fault Tree Analysis (FTA): A deductive, top-down method for analysing the causes of system failures by constructing a logical diagram of fault combinations.

References

1. Enhancing reliability in advanced manufacturing systems: A methodology for the assessment of detection and monitoring techniques. Journal of Manufacturing Systems (2025).
2. An Integrated FTA-FMEA Model for Risk Analysis of Engineering Systems: A Case Study of Subsea Blowout Preventers. Applied Sciences (2019).
3. A holistic FMEA approach by fuzzy-based Bayesian network and best–worst method. Complex & Intelligent Systems (2021).
4. A Novel FMEA Model Based on Rough BWM and Rough TOPSIS-AL for Risk Assessment. Mathematics (2019).
5. Agricultural Risk Management Using Fuzzy TOPSIS Analytical Hierarchy Process (AHP) and Failure Mode and Effects Analysis (FMEA). Agriculture (2020).
6. Fuzzy Risk Evaluation in Failure Mode and Effects Analysis Using a D Numbers Based Multi-Sensor Information Fusion Method. Sensors (2017).