基于异构粗糙神经网络集成的故障诊断研究

Fault diagnosis based on heterogeneous rough neural network ensemble

该方法不仅显著提高了神经网络的泛化能力,而且无需预先确定神经网络的拓扑结构,简单易用。设计了四种不同的诊断器在柴油机供油系统的标准样本集上进行的诊断测试实验,结果表明基于异构粗糙神经网络集成的故障诊断方法具有最好诊断正确率。

HRNNE can improve the generation capacity of the neural networks evidently, but also need not to determine the structures of the neural networks beforehand. It is very easy to use in practice. We design four different diagnosis classifiers to compare the diagnosis performance in the standard sample set of the fuel injection system of diesel engine. The experiment results show that HRNNE is the best one, which get high diagnosis accuracy.