Criticality Assessment Models for Failure Mode Effects and Criticality Analysis Using Fuzzy Logic
Traditional Failure Mode and Effects Analysis (FMEA) has shown its effectiveness in defining, identifying, and eliminating known and/or potential failures or problems in products, process, designs, and services to help ensure the safety and reliability of systems applied in a wide range of industries. However, its approach to prioritize failure modes through a crisp risk priority number (RPN) has been highly controversial. This paper proposes two models for prioritizing failures modes, specifically intended to overcome such limitations of traditional FMEA. The first proposed model treats the three risk factors as fuzzy linguistic variables, and employs alpha level sets to provide a fuzzy RPN. The second model employs an approach based on the degree of match and fuzzy rule-base. This second model considers the diversity and uncertainty in the opinions of FMEA team members, and converts the assessed information into a convex normalized fuzzy number. The degree of match (DM) is used thereafter to estimate the matching between the assessed information and the fuzzy number characterizing the linguistic terms. The proposed models are suitably supplemented by illustrative examples.
failure modes and effects analysis risk priority number degree of match fuzzy weighted geometric mean