A Novel Failure Mode and Effects Analysis Method Based on Fuzzy Evidential Reasoning Rules

Abstract

Failure mode and effects analysis (FMEA) is an effective reliability analysis technique and has been used for safety and dependability analysis in a wide range of fields. In the traditional FMEA, a method called risk priority number (RPN) has been widely used to determine the risk levels of failure modes. However, the method is deficient in dealing with imprecise data. To overcome that shortcoming, we propose a novel method based on fuzzy evidential reasoning rules to study the risk evaluation of failure modes in an uncertainty evaluation environment. The main contributions of this work are twofold: First, by analyzing the classical risk priority number method, we extract the reasoning knowledge from RPN method to construct fuzzy evidential reasoning rules for risk evaluation based on virtue of Dempster-Shafer evidence theory and fuzzy set theory; Second, the initial risk assessment is modeled with fuzzy form based on basic probability assignment (BPA) and fuzzy number, which can perfectly reflect the uncertainties in practice. The approach establishes a new reasoning model for fuzzy risk evaluation in FMEA. Finally, an example for risk evaluation of failure modes during general anesthesia process is given to illustrate the effectiveness of the proposed method.