基于入侵性野草算法的核动力装置故障诊断

段孟强; 袁灿

doi:10.7538/yzk.2015.49.04.0719

基于入侵性野草算法的核动力装置故障诊断

段孟强¹,
袁灿²

1.
海军驻葫芦岛431厂军事代表室,辽宁葫芦岛125005
2.
海军陆战学院,广东广州510430

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- 刊出日期: 2015-04-19

Fault Diagnosis of Nuclear Power Plant Based on Invasive Weed Optimization Algorithm

DUAN Meng-qiang¹,
YUAN Can²

1.
Nave Equipment Division Representative Office in 431 Factory of Huludao, Huludao 125005, China
2.
College of Marine Corps, Guangzhou 510430, China

摘要

摘要: 针对船用核动力装置故障原因与相应故障征兆之间并非完全一一对应的特点，提出了一种将入侵性野草算法和概率因果模型相结合的故障诊断方法，该方法将概率因果模型中的似然函数作为入侵性野草算法的适应函数，从而将复杂系统的故障诊断转化为优化问题。结果表明，该方法能用于诊断过程中出现的不确定性问题，也可实现通过多个征兆来诊断多个故障的目的，且具有较高的诊断可靠性与实用性。
- 入侵性野草算法 ,
- 概率因果模型 ,
- 故障诊断
Abstract: It is not completely accordant for fault reasons to match up corresponding symptoms of the marine nuclear power plant. A kind of fault diagnosis method was proposed, which is about invasive weed optimization algorithm combined with probability causal model. The probability causal model likelihood function was used as fitness function of the invasive weed optimization algorithm, after that the fault diagnosis of complex systems can be converted to optimization problem. The simulation results show that the method can not only be used for the process of diagnosis of uncertainty, but also for the purpose of multiple symptoms to multiple faults diagnose with high reliability and practicability.
- invasive weed optimization algorithm ,
- probability causal model ,
- fault diagnosis

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参考文献(14)

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基于入侵性野草算法的核动力装置故障诊断

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出版历程

Fault Diagnosis of Nuclear Power Plant Based on Invasive Weed Optimization Algorithm

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出版历程

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