基于球结构支持向量机的液压泵故障诊断研究  

Fault Diagnosis Study of Hydraulic Pump Based on Sphere-structured Support Vector Machines

作　　者：胡晓明[1] 

机构地区：[1]淮阴工学院,江苏淮安223003

出　　处：《煤矿机械》2008年第9期212-214,共3页Coal Mine Machinery

基　　金：淮阴工学院科研基金(HG0521)

摘　　要：针对目前故障诊断中难以获得大量故障数据样本的不足,提出球结构支持向量机故障分类算法,并运用其构成多故障分类器进行YB-6叶片泵的故障识别。试验表明该方法只需要少量的时域故障数据样本来训练分类器,不必进行信号预处理便可实现多故障的识别和诊断,在少样本情况下比BP神经网络具有故障分类能力强的优点。Proposes a algorithm of defect classification of sphere - structured suppot veeor machines, constituting the multi - breakdown sorter to carry on the YB - 6 hydraulic pump fault recognition in accordance with the insufficient data samples from fault diagnosis. The results show that training the classifier only needs a small quantity of fault data samples in time domain and does need signal preprocessing applied for multi - fault recognition and diagnosis. It has the advantage of strong ability of fault classification in the few sample situation compared to BP neural network.

关 键 词：球结构支持向量机 故障诊断 液压泵 神经网络 

分 类 号：TP306[自动化与计算机技术—计算机系统结构] TH3[自动化与计算机技术—计算机科学与技术]