Fractional Lower Order Feature Extraction Method of PF Components of Rolling Bearings

被引：0
|
作者：
Xu Q. [1 ,2 ]
Lin M. [1 ,2 ]
Liu K. [3 ]
Zhao W. [1 ,2 ]
机构：
[1] Key Laboratory of Green Printing & Packaging Materials and Technology, Qilu University of Technology(Shandong Academy of Sciences), Jinan
[2] School of Light Industry Science and Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan
[3] School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an
来源：
Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis | 2020年 / 40卷 / 06期
关键词：
Alpha stable distribution; Covariation; Feature extraction; Fractional lower order;
D O I：
10.16450/j.cnki.issn.1004-6801.2020.06.016
中图分类号：
学科分类号：
摘要：
In light of the degradation of the feature extraction from the second or higher-order statistic in the context of Alpha stable noise, a new feature extraction method of PF(product functions) components is introduced. The distribution properties of PF are validated by the tails and the estimation of α of the probability density function (PDF). Then, the bearing feature matrix is constructed based on the optimal fractional low-order statistics (FLOS) and covariant low-dimensional popular mapping matrix in order to reduce the error of the second-order and high-order statistics in describing the characteristics of signal components. Thus, various bearing faults are described accurately and intuitively. The comparisons with traditional methods show that lower-order features better describe the PF components with higher accuracy and clearer distinction. The feasibility indicates the advantages of the proposed method in practical applications. © 2020, Editorial Department of JVMD. All right reserved.
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页码：1141 / 1149
页数：8
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