作　　者： ; ; ;

机构地区： 华中科技大学机械科学与工程学院机械电子信息工程系

出　　处： 《机械工程学报》 2003年第8期65-70,共6页

摘　　要： 提出了基于核函数主元分析的齿轮故障诊断方法。该方法通过计算齿轮振动信号原始特征空间的内积核函数来实现原始特征空间到高维特征空间的非线性映射。通过对高维特征数据作主元分析,得到原始特征的非线性主元,以所选的非线性主元作为特征子空间对齿轮工作状态进行分类识别。用齿轮在正常状态、裂纹状态和断齿状态下的试验数据对该方法进行了检验,比较了主元分析与校函数主元分析的分类效果。结果表明,核函数主元分析能有效的检测裂纹故障的出现,正确区分不同的故障模式,更适于提取故障信号的非线性特征。 An approach to gear fault diagnosis is presented, which bases on kernel principal component analysis (KPCA). In this approach, the integral operator kernel functions is used to realize the nonlinear map from the raw feature space of gear vibration signals to high dimensional feature space. By performing PCA on the high dimensional feature sets, the nonlinear principal components of raw feature space are obtained. In succession, the selected nonlinear principal components are used to construct the feature subspace for classification of gearbox working conditions. The experimental data sets of gearbox working under three conditions: normal, tooth cracked and tooth broken are used to test the KPCA based method. The classification effect of KPCA based method is compared with that of PCA based method. The results indicate that the method can perform gear crack detection efficiently and can fulfill fault classification accurately, and it is more suitable for nonlinear feature extraction from fault signals.

关 键 词： 故障诊断 模式分类 特征提取 核函数主元分析 齿轮 振动

领　　域： [机械工程]