An integrated FMEA framework considering expert reliability for classification and its application in aircraft power supply system

Abstract

The risk assessment of the integrated drive generator (IDG) is a significant problem in flight safety that directly affects the operation of the aircraft power system However, the current IDG risk assessment methods still have many limitations, leading to risk identification and classification that is not accurate and scientific. Therefore, this paper constructs a failure mode and effect analysis (FMEA) framework for IDG risk identification and classification considering expert reliability in unbalanced hesitant fuzzy linguistic term sets (UHFLTSs) environment. First, since the UHFLTSs can effectively describe uncertain and imprecise linguistic information of decision makers (DMs) and adapt it to practical applications, it is specially chosen to depict the evaluation of DMs. Second, the importance of experts is fully assessed by combining expert reliability and expert weight since the variation in expert reliabilities may have a substantial impact on the final sorting outcomes. In addition, the group best–worst method (GBWM) is combined with the entropy method to determine the risk factor weight. Third, to build up the accuracy and stability of classification, a new integrated TOPSIS-ELECTRE TRI decision framework is proposed to classify failure modes more rationally. The sensitivity analysis of parameters demonstrates the robustness of the proposed method. Meanwhile, the effectiveness and superiority of the method are verified both qualitatively and quantitatively. By comparing with several typical sorting methods, the proposed method has more realistic and stable classification results.