PCA-based feature selection scheme for machine defect classification
PCA-based feature selection scheme for machine defect classification
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DOI：
10.1109/tim.2004.834070
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发表时间：
2004-12-01
影响因子：
5.6
通讯作者：
Gao, RX
中科院分区：
文献类型：
--
作者：
Malhi, A;Gao, RX
作为机器缺陷特征的各种特征的敏感度在不同的操作条件下可能会有很大的不同。因此，重要的是设计一个系统的特征选择方案，为选择最具代表性的特征进行缺陷分类提供指导。提出了一种基于主成分分析(PCA)的特征选择方法。在轴承试验台上用监督和非监督两种缺陷分类方法对该方法的有效性进行了实验验证。这项研究的目的是确定轴承缺陷的严重程度，其中关于缺陷条件的先验知识是可用的。与使用最初认为相关的所有特征相比，所提出的方案以更少的特征输入提供了更准确的缺陷分类。结果表明，该方法是一种有效的机器健康评估工具。
The sensitivity of various features that are characteristic of a machine defect may vary considerably under different operating conditions. Hence it is critical to devise a systematic feature selection scheme that provides guidance on choosing the most representative features for defect classification. This paper presents a feature selection scheme based on the principal component analysis (PCA) method. The effectiveness of the scheme was verified experimentally on a bearing test bed, using both supervised and unsupervised defect classification approaches. The objective of the study was to identify the severity level of bearing defects, where no a priori knowledge on the defect conditions was available. The proposed scheme has shown to provide more accurate defect classification with fewer feature inputs than using all features initially considered relevant. The result confirms its utility as an effective tool for machine health assessment.