A novel evidential FMEA method by integrating fuzzy belief structure and grey relational projection method. (January 2019)

• Record Type: Journal Article
• Authors: Li, Zhen; Chen, Luyuan
• Is Part Of: Engineering applications of artificial intelligence. Volume 77(2019)
• Page Start: 136
• Page End: 147
• Publication Date: 2019-01
• Subjects: Failure mode and effects analysis -- Risk priority numbers -- Fuzzy belief structure -- Dempster–Shafer evidence theory -- Grey relational projection method (GPRM); Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2018.10.005
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Failure mode and effects analysis (FMEA) has been applied extensively in reliability engineering domain. Risk priority number (RPN), which is the product of occurrence (O), severity (S) and detection ( D ) of a failure is the most important measure used in FMEA for prioritizing risk. In this paper, a novel evidential FMEA integrating fuzzy belief structure and grey relational projection method(GRPM) is presented to avoid the use of traditional RPN, for it has been criticized with some weaknesses. First, a new distribution form of assessments applying fuzzy belief structure to represent the experts' opinions in a more flexible and reasonable manner is constructed. Second, a new method to transform the experts' fuzzy opinions into basic probability assignments (BPAs) is proposed. Third, to address the problems existing in conflicting evidence combination, the differences of experts' authorities are taken into consideration, which is represented by the discounting coefficient. Besides, GPRM is adopted to address a more comprehensive assessments by fusing the 3 diverse criterion with weights respectively, which can overcome the limitations in traditional FMEA. An example is illustrated to show the practical application of the proposed FMEA methodology in engineering area.