A more realistic failure mode and effect analysis method considering causal relationships and consensus mechanism

Failure mode and effect analysis (FMEA) is crucial for system fault detection and reliability maintenance. Despite improvements in risk assessment accuracy in recent researches, certain issues in application persist. Expert preferences are still only partially expressed, and their information aggregation process is not practical enough. Complex system failure modes have several causal coupling interactions that are rarely considered by previous research but are essential to affect risk assessment. To address these concerns, this study creates an innovative FMEA method based on the T-spherical fuzzy cognitive map (T-SFCM) that takes consensus mechanisms into consideration. First, T-spherical fuzzy sets (T-SFSs) are used to deal with the ambiguous information generated by the expert expression and provide the expert with a more flexible expression domain. Second, for the T-spherical fuzzy environment, the expert opinion aggregation procedure is enhanced. The maximum deviation method (MDM) for figuring out the weights of risk factors and the expert weight calculation method is suggested. A consensus mechanism with minimum adjustment is proposed. The expert's hesitation in T-SFSs is considered in this procedure. Lastly, the fuzzy cognitive map (FCM) is extended to T-spherical fuzzy environment, and the T-SFCM is proposed. The causal network of fault scenarios is constructed to update the ranking results of failure modes after assessing the causal effects. A practical example illustrates the effectiveness and feasibility of the proposed method. The results indicate its superiority over other methods and its potential for application in various practical scenarios.