Abstract
Failure mode and effects analysis (FMEA) is a popular and useful approach applied to examine potential failures in different products, designs, processes, and services. As a vital index, the risk priority number (RPN) can determine the risk priorities of failure modes by some risk factors such as occurrence (O), severity (S), and detection (D). However, in FMEA, the traditional risk priority number approach has some shortcomings, especially in setting the weight of risk factors. This paper presents an improved risk priority number approach based on a fuzzy measure and fuzzy integral. A fuzzy measure is used to reflect the importance of the individual indicators and the indicator set and a fuzzy integral is a nonlinear function defined on the basis of fuzzy measure. The weights of risk factors given by domain experts are seen as fuzzy densities to generate a λ -fuzzy measure which can reflect the weights’ difference and relevance about risk factors. Then, the Choquet integral is used to fuse every value of risk factors about failure modes so as to obtain the comprehensive evaluation result. The result can reflect the comprehensive risk level, so it has a definite physical significance. Finally, an illustrative example and a comparison with another approach are given to show the effectiveness of the proposed approach in the paper.
Highlights
As an important branch of reliability analysis, failure mode and effects analysis (FMEA) is a methodical way to examine a proposed design in which failure is possible [1,2,3,4]
A method is proposed to improve risk priority number based on a fuzzy measure and fuzzy integral, which can effectively reflect the weights’ difference and relevance of risk factors
This paper proposes a method to improve Risk priority number (RPN) approach based on a fuzzy measure and fuzzy integral
Summary
As an important branch of reliability analysis, failure mode and effects analysis (FMEA) is a methodical way to examine a proposed design in which failure is possible [1,2,3,4]. RPN has some shortcomings especially in transforming linguistic variable and considering the difference of weight about risk factors. To some extent the failure mode with a high frequency is detected, so the weights of risk factors are related; in some systems decision makers pay more attention to the severity of a failure effect. A method is proposed to improve risk priority number based on a fuzzy measure and fuzzy integral, which can effectively reflect the weights’ difference and relevance of risk factors.
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