Amplitude filtering characteristics of singular value decomposition and its application to fault diagnosis of rotating machinery

被引：32

作者：

Guo, Mingjun ^{[1
]}

Li, Weiguang ^{[1
]}

Yang, Qijiang ^{[2
]}

Zhao, Xuezhi ^{[1
]}

Tang, Yalian ^{[3
]}

机构：

[1] South China Univ Technol, Sch Mech & Automot Engn, Guangzhou 510640, Peoples R China

[2] Guangzhou Maritime Univ, Sch Marine Engn, Guangzhou 510725, Peoples R China

[3] Guangdong Univ Finance, Dept Internet Finance & Informat Engn, Guangzhou 510521, Peoples R China

来源：

MEASUREMENT | 2020年 / 154卷

基金：

中国国家自然科学基金;

关键词：

Singular value decomposition (SVD); Effective singular values; Amplitude filter; Sliding bearing; Feature extraction; Axis orbit; Fault diagnosis; SVD;

D O I：

10.1016/j.measurement.2019.107444

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In this paper, two important properties of singular value decomposition (SVD) are deduced theoretically: (1) number law of singular values: one frequency corresponds to two singular values; (2) order rule of singular values: the larger the amplitude of signal is, the greater the corresponding two singular values are. The above two properties are collectively referred as amplitude filtering characteristics of SVD, and a signal separation algorithm (SVD-AF) based on this characteristic is proposed. Research shows that the algorithm shows excellent characteristics in both extracting multiple and single frequency components. What's more, the purified signal does not contain redundant components, nor does phase deviation occur. Finally, the proposed algorithm is used to purify axis orbits of the rotor of large sliding bearing test bed, the obtained axis trajectories are clear and concentrated, and the misalignment as well as rub impact fault of the rotor is identified successfully. (C) 2019 Elsevier Ltd. All rights reserved.

引用

页数：11

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