A novel method for failure mode and effects analysis using fuzzy evidential reasoning and fuzzy Petri nets

Failure mode and effects analysis (FMEA) has been broadly used in various industries to ensure the safety and reliability of high-risk systems. As a meritorious risk management tool, it can identify, evaluate and eliminate potential failure modes in a system for remedial actions. Nevertheless, the traditional FMEA has suffered from many deficiencies, especially in the assessment of failure modes, the weighting of risk factors, and the calculation of RPN. Therefore, this paper presents a novel FMEA method based on fuzzy evidential reasoning and fuzzy Petri nets (FPNs) to improve the classical FMEA. In this model, belief structures are used to capture the uncertainty and fuzziness of the subjective assessments given by experts and a rule-based FPN model is established to determine the risk priority of the failure modes identified in FMEA. An empirical case concerning the risk evaluation of a ship fire-safety system is provided to illustrate the practicality and effectiveness of the proposed FMEA. The results show that the new risk assessment method can produce more reliable risk ranking results of failure modes.