摘要
为解决水轮机故障诊断过度依赖专家经验且效率低的现状,利用水轮机故障历史数据和相关专家经验,建立故障树模型,寻求风险隐患和故障诊断之间的映射,通过故障树模型和贝叶斯网络模型的转换,利用贝叶斯网络的反向诊断技术深入研究根节点的概率重要度和敏感性,推理出导致故障发生的水轮机部件及故障原因,实现对水轮机的故障诊断。
In order to solve the current situation that hydraulic turbine fault diagnosis relies excessively on expert experience and has low efficiency, the historical data of hydraulic turbine fault and relevant expert experience were used to establish the fault tree model for seeking the mapping between risk hidden danger and fault diagnosis. Through the conversion of fault tree model and Bayesian network model, the probability importance and sensitivity of root node were deeply studied by using the reverse diagnosis technology of Bayesian network. Hydraulic turbine components and fault causes were inferred to realize the fault diagnosis.
作者
孙少楠
李博宇
聂相田
SUN Shao-nan;LI Bo-yu;NIE Xiang-tian(School of Water Conservancy,North China University of Water Resources and Electric Power,Zhengzhou 45ooo0,China)
出处
《水电能源科学》
北大核心
2023年第3期190-194,共5页
Water Resources and Power
基金
“新基建”项目(2020JGLX045)
国家自然科学基金资助项目(72271091,51709115)
河南省重点研发与推广专项(科技攻关)项目(182102210066)。
关键词
贝叶斯网络
故障诊断
故障树
敏感性分析
Bayesian networks
fault diagnosis
fault trees
sensitivity analysis