基于贝叶斯网络的复杂系统故障诊断的研究  

Study on Improving Bayesian Network-based Fault Diagnosis Technology

作　　者：王桂梅[1] 李艳强[1] 宋辉 李永利 

机构地区：[1]河北工程大学机电学院,河北邯郸056038 [2]河北钢铁集团,河北邯郸056000 [3]务腾咨询(上海)有限公司北京分公司,北京100025

出　　处：《煤炭与化工》2014年第11期4-7,共4页Coal and Chemical Industry

基　　金：河北省自然科学基金(E2012402027)

摘　　要：由于故障树方法在复杂系统的故障诊断应用中存在较大的局限性,其很难解决复杂系统在故障诊断中表现出的事件的多态性、信息的不确定性、故障逻辑关系的不确定性等问题,本文提出了一种新型的贝叶网络方法可以较好的解决这些问题。文中以酸洗线的风机系统的故障诊断为例,验证了这种新型方法的有效性。Due to the fault tree method had large limitations in the application of complex fault diagnosis system, which was difficult to solve complex system fault diagnosis polymorphism exhibited events, uncertainty of information, failure logical relationship and other issues, this paper presented a novel Bayesian network method, which could better solve these problems. Pickling line of the fan fault diagnosis system was taken as an example to verify the effectiveness of this new approach.

关 键 词：故障树 贝叶斯网络 故障诊断 

分 类 号：TG338[金属学及工艺—金属压力加工]