基于量子神经网络的电力电子电路故障诊断

Fault Diagnosis of Power Electronic Circuits Based on Quantum Neural Network

针对电力电子电路故障诊断时故障模式间存在交叉数据的模式识别问题,在量子计算和人工神经网络结合的基础上,提出了一种基于量子神经网络的故障诊断方法,并以双桥12相脉波整流电路为例进行故障诊断。实验结果表明:量子神经网络有一种固有的模糊性,它能将不确定性数据合理地分配到各故障模式中,从而使网络具有高性能、更好的鲁棒性和省时的特点,且能正确地识别大部分的样本故障模式,成功地完成电力电子电路的故障诊断。

In this paper, a method for fault diagnosis based on quantum neural network is presented on combination of quantum computation and artificial neural network, which aims at the pattern recognition problems of cross-data in fault modes, during the power electronic circuits fault diagnosis. By taking the Twin-bridge 12 phase pulse wave rectifier circuit as an example, the results show that quantum neural network has the characteristic of inherent ambiguity, which can assign the uncertainty data to each fault mode reasonably, making the network with the features of high capacity, better robust and timesaving. Meanwhile it can identify the majority of the sample fault modes correctly, and has accomplished the fault diagnosis of power electronic circuits successfully.