摘要
核电站系统复杂,需要采集和监测的变量较多,给核电站的故障诊断增加了困难。针对该问题提出集成学习算法,对核电站的失水事故、给水管道破裂、蒸汽发生器U型管破裂和主蒸汽管道破裂等4种典型故障进行训练学习,并分别在正常情况下和参数缺失情况下进行仿真实验。仿真结果表明,该算法在参数缺失的情况下仍能得到较好的诊断结果,具有良好的容错能力和泛化能力。
Nuclear power plant（NPP） is a very complex system,which needs to collect and monitor vast parameters,so it＇s hard to diagnose the faults of NPP.An ensemble learning method was proposed according to the problem.And the method was applied to learn from training samples which were the typical faults of nuclear power plant,i.e.,loss of coolant accident（LOCA）,feed water pipe rupture,steam generator tube rupture（SGTR）,main steam pipe rupture.And the simulation results were carried out on the condition of normal and invalid and absent parameters respectively.The simulation results show that this method can get a good result on the condition of invalid and absent parameters.The method shows very good generalization performance and fault tolerance.
出处
《原子能科学技术》
EI
CAS
CSCD
北大核心
2012年第10期1254-1258,共5页
Atomic Energy Science and Technology
关键词
核动力装置
故障诊断
集成学习
参数缺失
nuclear power plants
fault diagnosis
ensemble learning
invalid and absent parameters
