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A Failure Mode, Effect and Criticality Analysis (FMECA) is a methodology used to define, identify and eliminate known and or potential failures in order to enhance the product's reliability and quality. The Criticality Analysis plays an important role in FMECA. A method defines the failures' priority to find the most risk area. Traditionally, in automobile industry, the criticality assessment is based on the severity (S), frequency of occurrence (O), detection (D) of an item failure. The priority of the problems is articulated via the Risk Priority Number (RPN). This number is a product of the occurrence, severity and detection, i.e., the method assumes that the occurrence, severity and detection have same importance. Additionally, the component and system can only be in either of the two states: functioning or failed. However, it does not represent reality. In fact, failure mode is of fuzzy conception.

This paper presents a novel method of the FMEA of the turbocharged diesel engine system using fuzzy inferencing. It uses a membership function to represent information of FMEA (e.g. failure mode and failure effect) that is described by linguistic variables to the defined categories. The knowledge of experts is expressed in terms of if-then rules. Min-max inferencing is used to evaluate fuzzy model, and the Center of Area (centroid) method of defuzzification is adopted to obtain crisp results. A fuzzy-logic-based FMEA Two-stage Inferencing Model is developed. A prototype system based on the model has been constructed and used to conduct the FMEA of the turbocharger rotor-bearing subsystem. The results have shown the practicality of the method.