摘要
基于模型的故障诊断往往采用人工智能技术来处理不确定的知识和不完整的信息。概率推理法是一种处理不确定或不完整信息的方法,而贝叶斯网络是一种能够将它应用于实际的工具。提出了一种基于故障树和键合图理论来构建贝叶斯网络模型的新方法,并对系统进行故障诊断。实现了对引起系统或过程的异常行为的元件进行准确定位,并获得同时出现故障的元件对系统影响程度的大小,即为系统操作员提供了一个关于系统组件的优先级检查和维护计划。最后,对此方法的性能进行了仿真验证。
Model-based fault diagnosis often deals with uncertain knowledge and incomplete information by using articial intelligence techniques. Probability reasoning is a method to deal with uncertain or incomplete information, and Bayesian network is a tool that brings it into the real world application. A new method is proposed to construct a Bayesian network model based on fault tree and bond graph theory, and diagnosis of system. Realization of localizing faulty system components that causes the abnormal behaviors of a system or process, meanwhile, to get the faulty component on the system impact of the size, in other words, it provides system operators a priority checking and maintenance schedule for system components. Finally, the simulation results verify the performance of this method.
出处
《电测与仪表》
北大核心
2016年第2期21-26,共6页
Electrical Measurement & Instrumentation
基金
国家自然科学基金资助项目(61364010)
