A novel failure mode and effect analysis model using personalized linguistic evaluations and the rule-based Bayesian network

Abstract

Failure mode and effect analysis (FMEA) is a useful reliability-management instrument and has provided an effective risk assessment tool for engineering applications. With the advancement of technology, the engineering field has encountered an increasing number of failure modes. However, there is a lack of literature focused on evaluating large-scale failure modes. Additionally, the conversion of expert evaluations into numerical forms is primarily conducted by researchers, resulting in potential misunderstandings and information loss. Moreover, most FMEA methods rely only on the risk rankings, lacking further references to provide management recommendations. To address these research gaps, we propose a novel FMEA model. First, we employ a staged evaluation approach to accurately assess a large number of failure modes, which minimizes decision errors caused by evaluator fatigue. Second, to reduce the misinterpretation of expert evaluations and information loss, we improve the personalized individual semantics method to increase expert involvement, and introduce the inverse distance weighting method to minimize information loss. Third, we utilize the rule-based Bayesian network method to provide additional risk indicators. In addition to risk rankings, the output risk distribution offers valuable insights for engineering management. Finally, we apply the proposed model to a risk assessment project, providing suggestions on the safe operation of the floating production storage and offloading facility and vessels. Sensitivity and comparison analyses are conducted to demonstrate the validity and advantages of the proposed model.