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Improvement of fault diagnosis efficiency in nuclear power plants using hybrid intelligence approach

机译:使用混合智能方法提高核电厂故障诊断效率

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Different types of faults could occur in a nuclear power plant, and there was no direct correspondence between a specific fault and its symptoms. So, a hybrid intelligence approach is proposed for the fault diagnosis at a nuclear power plant. Depending up the symptoms observed and the progress of fault diagnosis process, different fault diagnosis technologies, such as artificial neural network, data fusion and signed directed graph, could be combined as appropriate to detect and identify different faults at local or global level in nuclear power plants. The effectiveness of hybrid intelligence approach in improving the fault diagnosis efficiency in nuclear power plants was verified through simulation experiments. (C) 2014 Elsevier Ltd. All rights reserved.
机译:核电站中可能发生不同类型的故障,并且特定故障与其症状之间没有直接对应关系。因此,提出了一种混合智能方法,用于核电站的故障诊断。根据观察到的症状和故障诊断过程的进展,可以适当地组合使用不同的故障诊断技术,例如人工神经网络,数据融合和带符号有向图,以在局部或全局范围内检测和识别核电中的不同故障。植物。仿真实验验证了混合智能方法在提高核电站故障诊断效率中的有效性。 (C)2014 Elsevier Ltd.保留所有权利。

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