考虑专家共识的改进FMEA风险评估方法

为了克服传统故障模式和影响分析（FMEA）应用过程中存在的不足，提出一种基于直觉乘法偏好关系的改进FMEA方法.首先，引入直觉乘法数表征故障模式和风险因子权重专家偏好信息，刻画评估信息的不确定性；其次，运用直觉乘法加权平均算子集结故障模式偏好矩阵，并分别提出直觉乘法数和直觉乘法偏好矩阵间的兼容测度，通过兼容性检验修正专家极端偏好信息以达成专家共识，确定可接受的故障模式群体偏好矩阵；再次，运用专家评估信息法和理想解模型法相结合的综合赋权法确定风险因子权重；最后，引入标准Manhattan距离，提出基于双向投影距离的改进优劣解距离法（TOPSIS）对故障模式进行风险排序.案例结果表明，该方法可有效提高风险评估准确度，为实际风险管理提供一定的指导意义.

Improved FMEA method for risk evaluation considering expert consensus

An improved FMEA method considering expert consensus based on intuitionistic multiplicative preference relations was proposed in order to overcome the shortcomings in application of traditional failure mode and effect analysis (FMEA) method. Firstly, the intuitionistic multiplicative number was introduced to represent the experts' preference information of failure modes and risk factor weights for dealing with the uncertainty of evaluation information. Secondly, the intuitionistic multiplicative weighted averaging operator was utilized to aggregate the preference matrices of failure modes. Meanwhile, the consensus measures of intuitionistic multiplicative numbers and intuitionistic multiplicative preference matrices were put forward to correct the experts' extreme preference information. Then the expert consensus was reached through the compatibility test, and the acceptable group preference matrix of failure modes was obtained. Thirdly, the risk factor weights were determined by using the combination weighting method based on evaluation information method and ideal solution optimization model. At last, the standard Manhattan distance was introduced to improve the technique for order preference by similarity to an ideal solution (TOPSIS) method, and an improved TOPSIS method based on bidirectional projection distance under intuitionistic multiplicative preference relation environment was proposed to determine the final risk ranking of failure modes. A case study was implemented to illustrate that the proposed method can improve the accuracy of risk evaluation and provide some guidance in practical risk management.