An Intuitionistic Evidential Method for Weight Determination in FMEA Based on Belief Entropy

Area of Science:

• Engineering Risk Analysis
• Decision Support Systems
• Uncertainty Quantification

Background:

• Failure Mode and Effects Analysis (FMEA) is a standard method for identifying and prioritizing potential failures.
• Determining appropriate team member weights in FMEA remains challenging, particularly when considering expert uncertainty.
• Existing FMEA methods often struggle to fully incorporate the subjective uncertainties inherent in expert decision-making.

Purpose of the Study:

• To propose a new method for determining team member weights in FMEA that accounts for expert uncertainty.
• To enhance the objectivity and comprehensiveness of FMEA by integrating advanced mathematical tools.
• To provide a data-driven approach for more reliable failure mode analysis.

Main Methods:

• A novel weighting determination method combining evidence theory, intuitionistic fuzzy sets (IFSs), and belief entropy.
• Incorporation of expert uncertainty directly into the weight calculation process.
• Development of a data-driven model for objective and reasonable weight assignment.

Main Results:

• The proposed method successfully integrates expert uncertainty into the FMEA weighting process.
• The approach provides objective and data-driven weights, considering risks more completely.
• A numerical example demonstrates the feasibility and effectiveness of the new method.

Conclusions:

• The novel FMEA weighting method effectively addresses the challenge of expert uncertainty in decision-making.
• This approach offers a more robust and reliable way to analyze and rank failure modes.
• The method's data-driven nature ensures objectivity and improves the overall quality of FMEA.