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Failure Mode and Effect Analysis (FMEA) is considered as a proactive risk prevention and control technique that has been widely applied to identify, assess and eliminate the risk of failure modes in various fields. Nevertheless, the interactions between risk indicators and decision maker's psychological behavior characteristics are seldom considered simultaneously in the current FMEA method. In this article, we develop a hybrid FMEA framework integrating generalized TODIM (an acronym in Portuguese of Interactive and Multi-criteria Decision Making) method, Choquet integral and Shapley index to remedy this gap. In the proposed FMEA framework, fuzzy measures and Shapley index are used to model the interaction relationships among risk indicators and to determine the weights of these indicators. The extended generalized TODIM method with fuzzy measure and Shapley index is presented to simulate the psychological behavior characteristics of FMEA team members. It is also applied to calculate the risk priority of each failure mode. Trapezoidal Fuzzy Numbers (TrFNs) are adopted to depict the uncertainty in the risk evaluation process. Furthermore, a new risk evaluation information fusion with TrFNs-WAIA (weighted arithmetic interaction averaging operator of the trapezoidal fuzzy numbers) operator based on Shapley Choquet is developed to aggregate individual risk evaluation information of FMEA team member into a group risk evaluation matrix, which considers the potential correlations among these members. Finally, a practical example of FMEA problem is presented to demonstrate the application and feasibility of the proposed hybrid FMEA framework, and comparison and sensitivity studies are also conducted to validate the effectiveness of the improved FMEA approach. [ABSTRACT FROM AUTHOR]

The Failure mode and effect analysis (FMEA) is an effective risk evaluation approach which has been widely used to assist in risk controlling in various workplaces. However, in practice, the conventional FMEA approach suffers from the drawbacks associated with the risk evaluation and priorization methods. In this paper, a novel risk priorization method for FMEA based on the extended MULTIMOORA (Multi-Objective Optimization by Ratio Analysis plus the Full Multiplicative Form) method is proposed. First, the interval type-2 fuzzy sets are applied to deal with the uncertainty of risk evaluation in FMEA. Second, the distance-based method is used to calculate the importance weight of each risk factor. Then, an extended MULTIMOORA method is presented to rank risk priority of each failure mode, in which the distance measure for interval type-2 fuzzy number is incorporated. Finally, a practical case in steel company is selected to illustrate the application and feasibility of the proposed approach. A comparative analysis is conducted to demonstrate the effectiveness of the developed risk priorization method. [ABSTRACT FROM AUTHOR]

Failure mode and effect analysis (FMEA) has been widely adopted to define, identity, and remove potential and recognized hazards. As an indicator in traditional FMEA, the risk priority number (RPN) is an effective tool for measuring risk and the calculation of RPN is also very simple. Nevertheless, there are many drawbacks in the conventional FMEA method. It is necessary to seek approaches that can make up for the deficiency of traditional FMEA method and strengthen assessment capability of ranking failure modes according to three relevant risk factors. This paper presents a way to combine interval type-2 fuzzy sets (IT2FSs) with evidential reasoning (ER) method, which is able to overcome some disadvantages of the conventional FMEA approach and deal with uncertainties more efficiently. First, we give a more precise expression of the risk factors in the form of IT2FSs and gain the relative weight of three risk factors. Second, one can judge the failure modes in relation to each risk factors with belief structures. Finally, the ER method is used to combine the belief structures under the weight of the three risk factors. To verify the feasibility of the method, an application for steam valve system is performed and the obtained results show the effectiveness of the method. • An interval type-2 fuzzy evidential reasoning method for FMEA is proposed. • Various assessments of team members are expressed by predefined linguistic terms. • A linear programming model to generate belief structure is developed. • An interval RPN is produced by fusing the information of three risk factors. • Demonstrate advantages of the proposed risk model with a steam valve system case. [ABSTRACT FROM AUTHOR]

• Multi-granular linguistic distribution evidential reasoning method is proposed. • Incomplete and uncertain evaluation is handled in MCGDM problems. • A novel comparison method for linguistic distribution assessments is introduced. • Developed a transformation technique for multi-granular linguistic distribution. • Renewable energy project risk assessment is conducted. Nowadays, renewable energy projects have constantly been emphasized and the assessment of potential risks has been considered as an indispensable activity prior to implementation of projects. To carry out a reasonable assessment, multi-granular linguistic distribution assessments (LDAs), an effective and uncertainty representation tool, are adopted to express and quantify opinions based on personalized individual linguistic term sets. Specifically, this study develops a novel uncertain multiple criteria decision making approach, named multi-granular linguistic evidential reasoning method, which can handle incomplete and personalized preferences. A novel comparison method for LDAs is first put forward based on numerical characteristics, namely an expectation value and central moment. To fuse the multi-granular LDAs, a lossless transformation technique is further introduced and some of itsessential properties are discussed. Finally, a case study on renewable energy project risk assessment is discussed to verify the feasibility of the proposed method. Besides, sensitivity analysis is conducted to investigate the sequencing stability and comparative analysis is included to highlight the superiority of the proposed method. [ABSTRACT FROM AUTHOR]