Failure Mode and Effect Analysis Using Large-Scale Group Decision Making and Normal Cloud Model.

Area of Science:

• Engineering
• Decision Science
• Risk Management

Background:

• Traditional Failure Modes and Effects Analysis (FMEA) struggles with heterogeneous data, large expert groups, and uncertainty propagation.
• Existing FMEA enhancements lack comprehensive solutions for complex decision-making environments.

Purpose of the Study:

• To propose an innovative and robust FMEA framework for Large Group Decision Making (LGDM) under uncertainty.
• To integrate diverse data types and enhance uncertainty propagation within the FMEA process.

Main Methods:

• Leveraging the Normal Cloud Model (NCM) for uncertainty modeling.
• Integrating LGDM with over 50 experts and handling heterogeneous data (numbers, intervals, linguistic terms).
• Implementing a four-step data preprocessing and a novel expert weight determination method.

Main Results:

• The proposed FMEA framework effectively handles diverse uncertainties and large expert groups.
• The method preserves and propagates uncertainty information, providing detailed quantitative analysis.
• Case studies and sensitivity analyses confirm the framework's effectiveness, robustness, and practicality.

Conclusions:

• The developed FMEA framework offers a scientifically advanced approach for complex system reliability.
• It enables more informed risk management decisions in high-stakes industries like aviation.
• The method demonstrates strong robustness and insensitivity to parameter fluctuations.