A Fault Diagnosis and Reliability Assessment Framework Based on Object-Oriented and Dynamic Bayesian Networks: A Case Study of a Crude Oil Pretreatment System

Abstract

Abstract. The crude oil pretreatment system is a critical step in crude oil processing, and it is responsible for removing impurities to ensure that the quality meets the standard. Its fault can lead to equipment damage, decreased product quality, production interruptions, and environmental pollution. Therefore, it is crucial to promptly identify the cause of failures and analyze the current reliability of the system. To address this urgent issue, this article designs a fault diagnosis model based on object-oriented Bayesian networks and a system reliability assessment model based on dynamic Bayesian networks. By utilizing a fault diagnosis Bayesian network, both single and multiple faults in the system are successfully identified. Subsequently, the output results of the fault diagnosis network are used as key information input into a reliability assessment Bayesian network, enabling an in-depth and comprehensive analysis of the system's reliability status during fault occurrences. This integrated method not only possesses fault diagnosis and reliability assessment capabilities but also precisely analyzes the weak links in the system, providing valuable and practical suggestions and guidance to on-site operators and maintenance teams.