New Approach For Failure Mode and Effect Analysis Based on Interval-Valued Intuitionistic Fuzzy Cloud Theory and Social Network Consensus Analysis - Wang, Lien; Liu, Hu-Chen; Liu , Wenheng (International Journal of Industrial Engineering: Theory, Applications and Practice, 2025)
Failure mode and effect analysis (FMEA) is a proactive risk assessment methodology extensively used across industries to enhance the safety and reliability of systems, products, and services. Classical FMEA predominantly relies on experts' subjective judgments, which inherently involve multiple types of uncertainty, including vagueness, hesitation, and randomness. However, existing FMEA studies seldom consider these types of uncertainty simultaneously. Moreover, experts' heterogeneous backgrounds and experiences can lead to divergent and inconsistent risk assessments. Few FMEA studies have investigated consensus-reaching mechanisms to address such an issue. Therefore, this paper presents a new FMEA framework integrating interval-valued intuitionistic fuzzy cloud (IVIFC) theory with social network consensus analysis to further enhance the performance of risk assessments. First, the IVIFC is adopted to describe experts' linguistic assessments. It blends the strength of interval-valued intuitionistic fuzzy sets in manipulating vagueness and hesitation with the advantage of the cloud model in reflecting the randomness of assessment information. Then, a social network consensus analysis model with maximum expert consensus is introduced to assist FMEA experts in reaching an agreement. Additionally, a hybrid method combining techniques for ordering preferences for similarity to ideal solutions and grey relational analysis is developed to derive the risk order of failure modes. Eventually, an empirical case in the robot-aided rehabilitation setting is presented to illustrate the developed FMEA model, with its effectiveness further substantiated through simulation and comparative analysis. The results show that the proposed model resolves major deficiencies of classical FMEA and offers a reliable solution for practical FMEA applications.