同济大学学报（自然科学版）2011，Vol.39Issue(11)：1699-1704,6.DOI:10.3969/j.issn.0253-374x.2011.11.024
采用动态故障树分析诊断系统故障的信息融合法
Information Fusion Method for System Fault Diagnosis Based on Dynamic Fault Tree Analysis
摘要
Abstract
An information fusion method was proposed to diagnose system faults with dynamic fault tree (DFT) analysis to improve the efficiency of system diagnosis, which made full use of the advantages of both DFT for modeling and Bayesian networks (BN) for the inference ability and incorporated system structure information as well as sensors data into fault diagnosis. All minimal cut sets were generated via an efficient zero-suppressed binary decision diagram, while the diagnostic importance factor of components and minimal cut sets were calculated using BN. Furthermore, these reliability analysis results together with the characteristic function of the system were updated after receiving the evidence data from sensorsand used to develop diagnostic decision algorithm to optimize system diagnosis. Then, a diagnostic decision tree was generated to guide the maintenance crew to recover a system. Finally,an example was given to illustrate the efficiency of this method.关键词
动态故障树/离散时间贝叶斯网络/诊断重要度/期望诊断代价Key words
dynamic fault tree/ discrete-time Bayesian network/ diagnostic importance/ the expected diagnosis cost分类
通用工业技术引用本文复制引用
段荣行,董德存,赵时旻..采用动态故障树分析诊断系统故障的信息融合法[J].同济大学学报（自然科学版）,2011,39(11):1699-1704,6.基金项目
国家"863"高技术研究发展计划资助项目(2007AA11Z247) （2007AA11Z247）
国家自然科学基金资助项目(61074139) （61074139）
