A New Method of Fault Feature Extraction Based on Hierarchical Dispersion Entropy

doi:10.1155/2021/8824901

	A New Method of Fault Feature Extraction Based on Hierarchical Dispersion Entropy
	Chen, Peng 1,2; Zhao, Xiaoqiang1,3,4 ; Jiang, HongMei1,3,4
	2021-02-26
发表期刊	Shock and Vibration
ISSN	1070-9622
卷号	2021
摘要	In the process of fault feature extraction of rolling bearing, the feature information is difficult to be extracted fully. A novel method of fault feature extraction called hierarchical dispersion entropy is proposed in this paper. In this method, the vibration signals firstly are decomposed hierarchically. Secondly, dispersion entropies of different nodes are calculated. Hierarchical dispersion entropy can realize the comprehensive feature extraction of the high- and low-frequency band information of vibration signals and overcome the problems that dispersion entropy and multiscale dispersion entropy are insufficient to extract the fault feature information of vibration signals. The feasibility of hierarchical dispersion entropy is obtained by analyzing the hierarchical dispersion entropy of Gaussian white noise and compared with the multiscale dispersion entropy of Gaussian white noise. Meanwhile, a fault diagnosis method for rolling bearings based on hierarchical dispersion entropy and k-nearest neighbor (KNN) classifier is developed. Finally, the superiority of the proposed fault diagnosis method is verified in the realization of fault diagnosis of the rolling bearing in different positions and different degrees of damage. © 2021 Peng Chen et al.
关键词	Dispersions Entropy Extraction Failure analysis Feature extraction Gaussian noise (electronic) Nearest neighbor search Roller bearings Signal processing White noise Fault diagnosis method Fault feature extractions Feature information Gaussian white noise K-nearest neighbor classifiers (KNN) Low frequency band Rolling bearings Vibration signal
DOI	10.1155/2021/8824901
收录类别	EI ; SCIE
语种	英语
WOS研究方向	Acoustics ; Engineering ; Mechanics
WOS类目	Acoustics ; Engineering, Mechanical ; Mechanics
WOS记录号	WOS:000627393100010
出版者	Hindawi Limited
EI入藏号	20211110077108
EI主题词	Fault detection
EI分类号	601.2 Machine Components ; 641.1 Thermodynamics ; 716.1 Information Theory and Signal Processing ; 802.3 Chemical Operations ; 921.5 Optimization Techniques ; 951 Materials Science
来源库	WOS
引用统计	被引频次：13[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/147727
专题	电气工程与信息工程学院
通讯作者	Zhao, Xiaoqiang
作者单位	1.Lanzhou Univ Technol, Coll Elect & Informat Engn, Lanzhou 730050, Peoples R China; 2.Lanzhou Petrochem Polytech, Coll Elect & Elect Engn, Lanzhou 730060, Peoples R China; 3.Lanzhou Univ Technol, Key Lab Gansu Adv Control Ind Proc, Lanzhou 730050, Peoples R China; 4.Lanzhou Univ Technol, Natl Expt Teaching Ctr Elect & Control Engn, Lanzhou 730050, Peoples R China
第一作者单位	电气工程与信息工程学院
通讯作者单位	电气工程与信息工程学院; 兰州理工大学
第一作者的第一单位	电气工程与信息工程学院
推荐引用方式 GB/T 7714	Chen, Peng,Zhao, Xiaoqiang,Jiang, HongMei. A New Method of Fault Feature Extraction Based on Hierarchical Dispersion Entropy[J]. Shock and Vibration,2021,2021.
APA	Chen, Peng,Zhao, Xiaoqiang,&Jiang, HongMei.(2021).A New Method of Fault Feature Extraction Based on Hierarchical Dispersion Entropy.Shock and Vibration,2021.
MLA	Chen, Peng,et al."A New Method of Fault Feature Extraction Based on Hierarchical Dispersion Entropy".Shock and Vibration 2021(2021).

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