Enhancing Failure Mode and E ect Analysis with Neutrosophic Inverse Soft Expert Sets

Abstract

In this study, we introduce a novel concept, the Neutrosophic Inverse Soft Expert Set (NISES), and apply it to
 the Failure Mode and E ect Analysis (FMEA) framework. Developed by NASA, FMEA is a robust tool for addressing
 industrial challenges. Our approach leverages the Evaluation based on Distance from Average Solution (EDAS) algorithm
 to solve FMEA problems. We implement this methodology in a real-world scenario involving a steam valve with eight
 distinct failure modes. Through rigorous analysis, we employ the Technique for Order of Preference by Similarity to Ideal
 Solution (TOPSIS) to rank the identified failure modes. Comparing our FMEA model, which integrates rough set theory and
 TOPSIS, with the conventional method, we demonstrate the superior e ciency of our approach. Additionally, we extend
 the application of Neutrosophic Inverse Soft Expert Sets using the Additive Ratio Assessment-Simplified Version (ARAS
SV) method. This innovative method facilitates a quantitative assessment of alternative options based on multiple attributes,
 allowing for a precise determination of the optimal choice.