A real-time fault diagnosis methodology of complex systems using object-oriented Bayesian networks
A real-time fault diagnosis methodology of complex systems using object-oriented Bayesian networks
复制标题
使用面向对象贝叶斯网络的复杂系统实时故障诊断方法
DOI：
10.1016/j.ymssp.2016.04.019
复制
发表时间：
2016-12-01
影响因子：
8.4
通讯作者：
Xie, Min
中科院分区：
文献类型：
--
作者：
Cai, Baoping;Liu, Hanlin;Xie, Min
贝叶斯网络（BN）是工业过程中不确定性概率推理的常用工具，但在故障诊断和可靠性评估等场合需要对大型复杂系统进行建模。为了降低贝叶斯网络故障诊断的复杂性，并报告立即发生的故障，提出了一种基于面向对象贝叶斯网络（OOBN）的复杂重复结构系统的实时故障诊断方法。建模方法包括两个主要阶段：离线OOBN建设阶段和在线故障诊断阶段。在离线阶段，传感器的历史数据和专家知识的收集和处理，以确定故障和症状，并基于OOBN的故障诊断模型的开发。在在线阶段，操作员的经验和传感器的实时数据被放置在OOBN进行故障诊断。根据工程经验，定义判断规则，得到故障诊断结果。(C)2016爱思唯尔有限公司版权所有
Bayesian network (BN) is a commonly used tool in probabilistic reasoning of uncertainty in industrial processes, but it requires modeling of large and complex systems, in situations such as fault diagnosis and reliability evaluation. Motivated by reduction of the overall complexities of BNs for fault diagnosis, and the reporting of faults that immediately occur, a real-time fault diagnosis methodology of complex systems with repetitive structures is proposed using object-oriented Bayesian networks (OOBNs). The modeling methodology consists of two main phases: an off-line OOBN construction phase and an on-line fault diagnosis phase. In the off-line phase, sensor historical data and expert knowledge are collected and processed to determine the faults and symptoms, and OOBN-based fault diagnosis models are developed subsequently. In the on-line phase, operator experience and sensor real-time data are placed in the OOBNs to perform the fault diagnosis. According to engineering experience, the judgment rules are defined to obtain the fault diagnosis results. (C) 2016 Elsevier Ltd. All rights reserved.