Failure mode and effects analysis using fuzzy evidential reasoning approach and grey theory

Failure mode and effects analysis (FMEA) is a methodology to evaluate a system, design, process or service for possible ways in which failures (problems, errors, etc.) can occur. The two most important issues of FMEA are the acquirement of FMEA team members' diversity opinions and the determination of risk priorities of the failure modes that have been identified. First, the FMEA team often demonstrates different opinions and knowledge from one team member to another and produces different types of assessment information because of its cross-functional and multidisciplinary nature. These different types of information are very hard to incorporate into the FMEA by the traditional model and fuzzy logic approach. Second, the traditional FMEA determines the risk priorities of failure modes using the risk priority numbers (RPNs) by multiplying the scores of the risk factors like the occurrence (O), severity (S) and detection (D) of each failure mode. The method has been criticized to have several shortcomings. In this paper, we present an FMEA using the fuzzy evidential reasoning (FER) approach and grey theory to solve the two problems and improve the effectiveness of the traditional FMEA. As is illustrated by the numerical example, the proposed FMEA can well capture FMEA team members' diversity opinions and prioritize failure modes under different types of uncertainties.