FMEA Assessment Under Heterogeneous Hesitant Fuzzy Preference Relations: Based on Extended Multiplicative Consistency and Group Decision Making

Failure mode and effects analysis (FMEA) is a reliability analysis method that analysis all possible failure modes for each product in a system and all possible effects of failure modes on the system, to classify each failure mode and to propose solutions and preventative measures. It is undeniable that the traditional FMEA method has been widely criticized for its simple scoring and single algorithm. To improve the usability of FMEA, hesitation fuzzy preference relation sets (HFPRs) based on hesitant fuzzy sets have been introduced into FMEA research because of their good fuzziness and uncertainty properties. Most existing consistency-based algorithms for HFPR processing, however, do not consider the possible coherence deviation of the reluctant fuzzy set itself, which includes multiplicative consistency (MC) theory, which will result in the reduced accuracy of results from such algorithms, in addition to not supporting group decision-making well in heterogeneous environments. At the same time, when building a group consensus, constantly adjusting HFPRs through group decision feedback can easily lead to conservative or radical scoring by experts. Therefore, an excellent hybrid FMEA assessment method is studied in this paper. In this approach, an extended multiplicative consistency equation is constructed by extending the applicability of MC in the treatment of HFPR, and on this basis, a mathematical model with the ability to deal with sets of heterogeneous fuzzy preference relations (H-HFPRs) is constructed. Lastly, based on the predictability principle of the occurrence level (O), a scoring correction algorithm is constructed based on group consensus theory to reduce the conservative or aggressive bias of the expert group in the results. The new method was used in the risk assessment of Change oilfield subsea pipeline engineering, and the results were compared with several existing methods to verify the effectiveness and advancement of the proposed method.