基于特征评估与核主分量分析的齿轮故障分类方法  

Method of Gear Fault Classification based on Feature Evaluation and Kernel Principal Component Analysis

作　　者：瞿雷[1] 戴光昊[2] 王琇峰[1] 沈玉娣[1] 

机构地区：[1]西安交通大学机械工程学院,陕西西安710049 [2]中国船舶重工集团公司第七〇三研究所,黑龙江哈尔滨150078

出　　处：《机械传动》2014年第11期105-110,共6页Journal of Mechanical Transmission

基　　金：高效高精度齿轮机床产品技术创新平台(项目编号:2012ZX04012032)

摘　　要：针对无先验知识模式下机械故障特征的选择、融合存在盲目性、片面性,提出了一种基于特征评估与核主分量分析的齿轮故障特征提取与分类方法。该方法采用小波包分解对原始信号进行分解,分别提取原始信号和各分解信号的时域指标组成联合特征,然后确定了稳定性门限值与敏感性筛选比例因子,采用稳定性与敏感性联合评估方法对特征进行评估,并利用核主分量分析方法提取剩余联合特征中的非线性特征,实现不同齿轮故障状态的分类。实验结果表明,这种集成了小波包分解、特征联合评估方法和核主分量分析的齿轮故障分类方法能够更好地提取齿轮故障的特征信息,从大量的故障特征中剔除不稳定与不敏感的劣质特征,明显改善了核主分量分析提取齿轮故障非线性特征的效果。For the blindness and one-sidedness of selection and fusion of mechanical fault features without priori knowledge,a novel method of gear fault feature extraction and classification based on feature evaluation and kernel principal component analysis is presented,where the original signals are decomposed with wavelet pocket decomposition（WPD）,and the features in time domain are extracted from the original signals and each decomposed signal to compose the combined features.Furthermore,the threshold value for stability and the filtering scale factor for sensitivity are confirmed to evaluate the features by the combined method with stability and sensitivity,and the nonlinear features are extracted from the residual features by using the method of kernel principal component analysis（KPCA）to realize the classification of different fault conditions.The experimental results of gearbox demonstrate that the method integrating WPD,combined feature evaluation method and KPCA,could better extract the feature information of gear fault,remove the unstable and insensitive ones from a large number of features,and obviously improve the result of nonlinear feature extraction of gear fault for KPCA.

关 键 词：特征评估 核主分量分析 小波包分解 特征提取 齿轮 

分 类 号：TH132.41[机械工程—机械制造及自动化] TH165.3