摘要
根据抱砖机液压夹紧系统的复杂性、种类多、工作环境恶劣等特征,本文采用一种基于主元分析模型PCA与学习矢量量化(LVQ)神经网络结合的液压故障诊断模型诊断抱砖机夹紧系统故障。此模型拥有主元分析的降维、计算速度快的优点,同时又有LVQ提高故障诊断效率和准确率。
According to the complexity,variety and working environment of the hydraulic clamping system of the brick-carrying machine,a hydraulic fault diagnosis model based on PCA and learning vector quantization(LVQ)neural network was adopted to diagnose the fault of clamping system of brick holding machine.This model had the advantages of dimension reduction and fast calculation of principal component analysis,and at the same time had the advantage of high recognition rate of LVQ.The results showed that using this method could greatly improve the efficiency and accuracy of fault diagnosis.
作者
赵阳
ZHAO Yang(College of Mechanical Engineering,North China University of Water Resources and Electric Power University,Zhengzhou Henan 450000)
出处
《河南科技》
2018年第35期26-28,共3页
Henan Science and Technology
关键词
抱砖机
液压系统
主元分析
故障诊断
brick holding machine
thrust hydraulic system
PCA-BP neural networks
fault diagnosis
