Failure Mode, Effects, and Criticality Analysis Improvement Using a Fuzzy Criticality Assessment Based Approach

Abstract

Failure mode, effects, and criticality analysis (FMECA) is a proactive quality tool that allows the identification and prevention of the potential failure modes of a process or product. In a conventional FMECA, for each failure mode, three risk parameters, namely frequency, non-detection, and severity are evaluated and a risk priority number (RPN) is calculated by multiplying these parameters to assess one signal criticality. However, in many cases, it suffers from some shortcomings regarding the decision-making and the situation where the information provided is ambiguous or uncertain. Thus, in this paper, a fuzzy criticality assessment-based approach is used to improve the exploitation of the FMECA method. The new model is based on replacing the traditional calculation of criticality (RPN) with a fuzzy inference engine. The authors used fuzzy logic where the different parameters are shown as members of a fuzzy set, which is fuzzified by using appropriate membership functions to evaluate the criticality and then prioritizing failure causes as well preferring actions for controlling the risks of undesirable scenarios.