基于故障树与键合图的贝叶斯网络故障诊断

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。

Fault Tree and Bond Graph Based Bayesian Network for Fault Diagnosis

Model-based fault diagnosis using arti?cial intelligence techniques often deals with uncertain knowledge and incomplete information. 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 cause the abnormal behaviors of a system or process, and at the same time 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, using simulation to verify the performance of this method.