基于神经网络与表决融合的核动力装置故障诊断方法

针对单神经网络（ANN）故障诊断方法的不足，将多神经网络诊断与表决融合方法结合起来，研究了基于多神经网络与表决融合的核动力装置故障诊断方法。在该方法中，多个不同类型的神经网络训练后用于核动力装置的故障诊断。选择对核动力装置安全有重要影响的运行参数作为各神经网络的输入变量，神经网络的输出是核动力装置的故障模式。用表决融合方法对不同神经网络的诊断结果进行融合，从而得到核动力装置故障诊断的最后结果。利用核动力装置典型的运行模式来验证所提出的诊断方法的效果。结果表明，与单神经网络相比，该方法可提高核动力装置故障诊断结果的精度和可靠性。

Fault Diagnosis Method for Nuclear Power Plants Based on Neural Networks and Voting Fusion

A new fault diagnosis method based on multiple neural networks (ANNs) and voting fusion for nuclear power plants (NPPs) was proposed in view of the shortcoming of single neural network fault diagnosis method. In this method, multiple neural networks that the types of neural networks were different were trained for the fault diagnosis of NPP. The operation parameters of NPP, which have important affect on the safety of NPP, were selected as the input variable of neural networks. The output of neural networks is fault patterns of NPP. The last results of diagnosis for NPP were obtained by fusing the diagnosing results of different neural networks by voting fusion. The typical operation patterns of NPP were diagnosed to demonstrate the effect of the proposed method. The results show that employing the proposed diagnosing method can improve the precision and reliability of fault diagnosis results of NPPs.