Build a Bayesian Network from FMECA in the Production of Automotive Parts: Diagnosis and Prediction

Abstract

Failure Modes, Effects and Criticality Analysis (FMECA) is one of the well-known methods of quality management that is used for continuous improvement in product or process design. This method uses linguistic expressions and has good information about cause-effect chains. However, it lacks probabilistic information. Transforming it into a Bayesian Network (BN) makes it possible to be used in maintenance for both diagnosis and prediction. The purpose of this paper is to develop a method that uses as much information as possible from FMECA, including frequency and detection to precisely make the configuration of the BN. To build a BN’s structure from FMECA, we elaborate a tool to do it systematically. Moreover, we develop an algorithm to set the parameters of a BN obtained. Elicitation methods based on expert knowledge are used when data is not sufficient. A case study of FMECA in the automotive industry is introduced to verify the applicability of the proposed method in an industrial environment.

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