摘要
结合主分量分析（Principal Component Analysis, PCA）和子空间法研究了基于主分量子空间的设备状态诊断，探讨了压缩子空间和类属子空间两种主分量子空间结构来表达和分类设备的状态。所提出的设备状态诊断方法依靠PCA可以提取稳定有效的设备状态低维特征表示，依靠子空间法能够以低代价有效辨识设备状态。以汽车变速齿轮箱的疲劳状态诊断为例分析表明，两种主分量子空间方法都获得了良好的结果，且具有各自的优点，可以有效用于设备的状态监测和诊断中。
Abstract
Machine condition diagnosis is studied by combining the principal component analysis (PCA) and the subspace methods. In the principal component subspace-based method, two subspace structures, called information compression subspace and class-specific subspace, are presented to represent and classify machine condition pattern. The proposed methods can extract effective and stable low-dimensional features of machine condition by the PCA, and effectively identify machine condition with low computational complexity based on the subspace methods. Experimental analysis with fatigue condition diagnosis of an automobile transmission gearbox shows that two principal component subspace-based methods have achieved excellent results with respective merit, and can be effectively applied to machine condition monitoring and diagnosis.
关键词
主分量分析 /
子空间 /
设备状态诊断 /
齿轮箱
{{custom_keyword}} /
Key words
principal component analysis /
subspace /
machine condition diagnosis /
gearbox
{{custom_keyword}} /
刘永斌;何清波;吴强;李鹏;胡飞;孔凡让.
基于主分量子空间的设备状态诊断研究[J]. 振动与冲击, 2011, 30(10): 265-269
HE Qing-bo; LIU Yong-bin; WU Qiang; LI Peng; HU Fei;KONG Fan-rang.
Machine condition diagnosis based on principal component subspace[J]. Journal of Vibration and Shock, 2011, 30(10): 265-269
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
{{custom_fnGroup.title_cn}}
脚注
{{custom_fn.content}}
PDF(2000 KB)
