Algorithm Research & Explore
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460-464

Rotor fault data set classification method based on EEMD energy moment and neighborhood rough sets

Sun Zejin
Zhao Rongzhen
School of Mechanical & Electronical Engineering, Lanzhou University of Technology, Lanzhou 730050, China

Abstract

Aiming at the problem of low recognition accuracy of rotating machinery faults, this paper proposed a rotor system fault identification method based on the combination of EEMD with energy moment and NRS. Firstly, this method used the EEMD to decompose the non-stationary vibration signal into several stable IMF and calculated the energy moment of the IMF component. This energy moment was used as the condition attribute to describe the fault state to establish the fault identification decision table. Then this paper used the neighborhood rough set to perform attribute reduction on the decision table to eliminate the redundant attribute. Finally, it used the reduced sensitivity feature subsets as input into decision tree C4.5 algorithm for recognition. The experimental results of fault feature set verify effectiveness of this method.

Foundation Support

国家自然科学基金资助项目(51675253)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.07.0532
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 2
Section: Algorithm Research & Explore
Pages: 460-464
Serial Number: 1001-3695(2020)02-030-0460-05

Publish History

[2020-02-05] Printed Article

Cite This Article

孙泽金, 赵荣珍. 基于EEMD能量矩与邻域粗糙集的转子故障数据集分类方法 [J]. 计算机应用研究, 2020, 37 (2): 460-464. (Sun Zejin, Zhao Rongzhen. Rotor fault data set classification method based on EEMD energy moment and neighborhood rough sets [J]. Application Research of Computers, 2020, 37 (2): 460-464. )

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  • Application Research of Computers Monthly Journal
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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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