基于集成学习的核电站故障诊断方法OA北大核心CSCDCSTPCD
Fault Diagnosis Method for Nuclear Power Plant Based on Ensemble Learning
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 th…查看全部>>
慕昱;夏虹;刘永阔
哈尔滨工程大学 核安全与仿真技术国防重点学科实验室,黑龙江哈尔滨150001哈尔滨工程大学 核安全与仿真技术国防重点学科实验室,黑龙江哈尔滨150001哈尔滨工程大学 核安全与仿真技术国防重点学科实验室,黑龙江哈尔滨150001
信息技术与安全科学
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
《原子能科学技术》 2012 (10)
1254-1258,5
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