为实现旋转机械故障诊断方法的自动化、高精度及通用性,提出基于线性局部切空间排列(Linear Local Tangent Space Alignment,LLTSA)维数化简的故障诊断模型.首先结合经验模式分解(Empirical Mode Decomposition,EMD)和自回归(Autoregression,AR)模型系数构造全面表征不同故障特性的混合域特征集,再利用LLTSA将高维混合域特征集化简为故障区分度更好的低维特征矢量,并输入到最近邻分类器(K-nearest Neighbors Classifier,KNNC)中进行故障模式识别.所提出的诊断模型充分融合混合域特征融合在故障特征的全面提取、LLTSA在信息的有效化简及KNNC在分类决策方面的优势,实现诊断方法的自动化、高识别率及较好的通用性.用深沟球轴承不同部位、不同程度故障诊断实例验证该模型的有效性.%Based on dimension reduction using linear local tangent space alignment ( LLTSA) , a novel fault diagnosis model was proposed to achieve automatic, high-precise and general fault diagnosis of rotating machinery. With this model, mixed-domain feature sets of training and test samples were constructed to characterize the property of each kind of fault comprehensively by the fusion of empirical mode decomposition ( EMD) and autoregression (AR) model coefficients. After that, LLTSA was introduced to automatically compress the high-dimensional eigenvectors of training and test samples into the low-dimensional eigenvectors which have better discrimination. Finally, the low-dimensional eigenvectors of training and test samples were input into K-nearest neighbors classifier ( KNNC ) to carry out fault diagnosis. Comparing to the existing approaches, the proposed diagnosis model combines the advantages of mixed-domain features fusion in extensive extraction of fault feature, LLTSA in effective compression of fault information and KNNC in classification decision-making, and realizes the automation, high-precision and generality of fault diagnosis method. The diagnosis examples on different fault positions and severities of deep groove ball bearings validate the effectivity of proposed model.
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