CAJ | 학술논문

学习向量量化(LVQ)神经网络可以通过监督学习完成对输入向量模式的准确分类,提出了一种基于改进的LVQ神经网络的发动机故障诊断方法,介绍了LVQ神经网络及其改进的学习算法.以长城哈佛GW2.8TC型发动机为实验对象,让发动机在怠速状况下,对发动机进行故障设置,利用金德KT600电脑故障诊断仪采集发动机数据流,运用改进的LVQ神经网络建立诊断模型,诊断结果表明,改进的LVQ神经网络能对发动机故障做出正确分类,准确率比较高.
학습향량양화(LVQ)신경망락가이통과감독학습완성대수입향량모식적준학분류,제출료일충기우개진적LVQ신경망락적발동궤고장진단방법,개소료LVQ신경망락급기개진적학습산법.이장성합불GW2.8TC형발동궤위실험대상,양발동궤재태속상황하,대발동궤진행고장설치,이용금덕KT600전뇌고장진단의채집발동궤수거류,운용개진적LVQ신경망락건립진단모형,진단결과표명,개진적LVQ신경망락능대발동궤고장주출정학분류,준학솔비교고.
Since learning vector quantization (LVQ) neural network can classify input vector pattern accurately by super-vised learning,the fault diagnosis method for engines based on LVQ neural network is proposed. LVQ neural network and its im-proved learning method are introduced. Taking Great Wall Harvard GW2.8TC engine as the experimental subject,faults are set for the engine under idle speed condition. The data stream of the engine is collected by using Kinder KT600 computer fault diag-nosis tester. The diagnosis model was established by using the improved LVQ neural network. The diagnosis results show that the improved LVQ neural network can classify engine faults accurately,and the precision rate is relatively high.