Failure mode and effects analysis (FMEA) for risk assessment based on interval type-2 fuzzy evidential reasoning method

• Published in: Applied soft computing Vol. 89; p. 106134
• Main Authors: Qin, Jindong, Xi, Yan, Pedrycz, Witold
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.04.2020
• Subjects: Evidential reasoning (ER); Failure mode and effects analysis (FMEA); Interval type-2 fuzzy sets (IT2FSs); Risk assessment; Failure mode and effects analysis (FMEA); Evidential reasoning (ER); Interval type-2 fuzzy sets (IT2FSs); Risk assessment
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2020.106134

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.