基于敏感特征选择和非线性特征融合的滚动轴承故障诊断方法

Peng Liu, Hong-ru Li, P. Ye
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引用次数: 4

摘要

针对滚动轴承故障诊断中特征集存在非敏感特征和维度过高的问题,提出了一种基于敏感特征选择和非线性特征融合的滚动轴承故障诊断特征提取新方法。利用CDET从高维特征集中选择对故障严重程度敏感的特征,并根据敏感程度对所选敏感特征进行加权。利用LPP对加权敏感特征子集进行降维压缩,得到压缩后的更敏感特征子集,通过提高类间散点和类内散点来增强故障分类的分类能力。与不进行特征选择的基于直接LPP的原始高维特征集分类结果相比,该方法的分类结果具有更高的紧凑性和更高的计算效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Method for Rolling Bearing Fault Diagnosis Based on Sensitive Feature Selection and Nonlinear Feature Fusion
To solve the problem that there were non-sensitive features and over-high dimensions in the feature set of fault diagnosis, a new feature extraction method based on sensitive feature selection and nonlinear feature fusion for rolling element bearing fault diagnosis was proposed. CDET was utilized to choose features sensitive to fault severity from the high dimensional feature set, and weighted the selected sensitive features by their sensitive degree. The weighted sensitive feature subset was compressed with LPP to reduce its dimensions and get the compressed more sensitive feature subset, which can enhance the discrimination between all classes by improving the between-class scatter and within-class scatter for fault classification. Compared with the classification result of the original high dimensional feature set based on direct LPP without feature selection, the classification result of the proposed method has the advantages of higher compactness and higher computational efficiency.
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