Using fuzzy logic to reduce risk uncertainty in failure modes and effects analysis

Abstract

Failure modes and effects analysis (FMEA) is used in product design as a systematic analysis tool that aims to identify and evaluate potential failure modes, their causes and effects. However, vital information for FMEA, such as consumer needs and expert opinions, is often uncertain or vague in the product design phase. On the other hand, fuzzy logic is a technique that has been used to overcome the absence of concrete data and generate robust results to drive decisions from uncertainties. In this sense, this paper proposes a methodology that combines the concepts of fuzzy logic and product FMEA. In the proposed approach, the parameters severity, occurrence and detectability are evaluated in a fuzzy inference system. Its applicability was investigated with the help of an illustrative case study. Fuzzy FMEA was carried out to prioritize risks on one module of a Jerusalem artichoke processing equipment. The results provide an alternate ranking to that obtained by the traditional method. The conclusion is that the proposed methodology enables experts to combine occurrence probability, severity and failure modes detectability in a more flexible and realistic manner by using their judgement and experience.