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Detrended Fluctuation Analysis and Hough Transform Based Self-Adaptation Double-Scale Feature Extraction of Gear Vibration Signals

作者	Wang, JiaQing^[1];Xiao, Han^[2];Lv, Yong^[3];Wang, Tao^[4];Xu, Zengbing^[5]
文献类型	期刊
机构	[1]Wuhan Univ Sci & Technol, Hubei Prov Key Lab Machine Transmiss & Mfg Engn, POB 222, Wuhan 430081, Hubei, Peoples R China.; [2]Wuhan Univ Sci & Technol, Hubei Prov Key Lab Machine Transmiss & Mfg Engn, POB 222, Wuhan 430081, Hubei, Peoples R China.; [3]Wuhan Univ Sci & Technol, Hubei Prov Key Lab Machine Transmiss & Mfg Engn, POB 222, Wuhan 430081, Hubei, Peoples R China.; [4]Wuhan Univ Sci & Technol, Hubei Prov Key Lab Machine Transmiss & Mfg Engn, POB 222, Wuhan 430081, Hubei, Peoples R China.; [5]Wuhan Univ Sci & Technol, Hubei Prov Key Lab Machine Transmiss & Mfg Engn, POB 222, Wuhan 430081, Hubei, Peoples R China.; ↓
通讯作者	Xiao, H (reprint author), Wuhan Univ Sci & Technol, Hubei Prov Key Lab Machine Transmiss & Mfg Engn, POB 222, Wuhan 430081, Hubei, Peoples R China.
期刊名称	SHOCK AND VIBRATION影响因子和分区
年	2016
卷	2016
增刊	正刊
收录情况	SCI(E)(WOS:000370400200001) EI(20160501869458)
期刊等级	B
所属部门	机械自动化学院；研究生院
百度学术	Detrended Fluctuation Analysis and Hough Transform Based Self-Adaptation Double-Scale Feature Extraction of Gear Vibration Signals
语言	外文
ISSN	1070-9622
DOI	10.1155/2016/3409897
被引频次	1
人气指数	47
浏览次数	34
基金	National Natural Science Foundation of China (NSFC) [51105284, 51475339, 51375154, 51405353]
摘要	This paper presents the analysis of the vibration time series of a gear system acquired by piezoelectric acceleration transducer using the detrended fluctuation analysis (DFA). The experimental results show that gear vibration signals behave as double-scale characteristics, which means that the signals exhibit the self-similarity characteristics in two different time scales. For further understanding, the simulation analysis is performed to investigate the reasons for double-scale of gear's fault vibration signal. According to the analysis results, a DFA double logarithmic plot based feature vector combined with scale exponent and intercept of the small time scale is utilized to achieve a better performance of fault identification. Furthermore, to detect the crossover point of two time scales automatically, a new approach based on the Hough transform is proposed and validated by a group of experimental tests. The results indicate that, comparing with the traditional DFA, the faulty gear conditions can be identified better by analyzing the double-scale characteristics of DFA. In addition, the influence of trend order of DFA on recognition rate of fault gears is discussed.