基于混合神经网络的核动力装置并发故障诊断研究

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Intercurrent Fault Diagnosis of Nuclear Power Plants Based on Hybrid Artificial Neural Network

College of Naval Architecture and Power, Naval University of Engineering, Wuhan, 430033, China

摘要: 在对ART-2网络和并行前馈反向神经网络(BP)模型进行分析的基础上,提出了一种针对核动力装置并发故障进行诊断的复合神经网络。先运用ART-2网络识别单一故障,再使用并行BP实现并发故障和新故障的分离。仿真实验表明,所构建的复合神经网络,克服了单一神经网络难于识别并发故障和新故障的缺陷,能够有效地实现并发故障和新故障的诊断识别。
- 核动力装置 /
- 并发故障 /
- 混合神经网
Abstract: Based on the analysis of the structure of ART-2 and parallel BP neural network,a hybrid artificial neural network is proposed aiming at the intercurrent faults diagnosis of nuclear power plants.Firstly the ART-2 net is used to identify the single fault,then the parallel BP net is used to distinguish intercurrent faults from new fault.The simulation shows that,the hybrid artificial neural network resolves the problem of single neural network in distinguishing intercurrent faults from new fault,and can diagnose the intercurrent fault and new fault efficiently.
- Nuclear power plant /
- Intercurrent fault /
- Hybrid artificial neural network