Fault Diagnosis Model Based on NRS and EEMD for Rolling-element Bearing

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	Fault Diagnosis Model Based on NRS and EEMD for Rolling-element Bearing
	Lian, Jin; Zhao, Rongzhen
	2017
关键词	NRS EEMD rolling-element bearing fault diagnosis
页码	999-1003
英文摘要	Considering the fact that the early fault of the rolling-element bearing is difficult to identify correctly, a neighborhood rough set (NRS) and ensemble empirical mode decomposition (EEMD) fault diagnosis model of rolling-element bearing combination is come up with. First of all, the original signal of the vibration is decomposed by EEMD to obtain a number of IMF components, and extracted time-features from the first 3 IMF components in the time domain to form the original features. Then, NRS is used to reduce the attributes of original features, eliminate the redundant information, and put the sensitive reduced feature attributes into the SVM classifier for fault identification. This model is applied to typical rolling-element bearing fault diagnosis experiments, which shows that the NRS is used to select a large number of features containing abundant fault information from the original features. The method not only reduces the complexity of the classification algorithm, but also enhances the accuracy of the fault identification by 5%, which provides a new approach of analysis model for rolling-element bearing fault diagnosis.
会议录	2017 PROGNOSTICS AND SYSTEM HEALTH MANAGEMENT CONFERENCE (PHM-HARBIN)
会议录出版者	IEEE
会议录出版地	345 E 47TH ST, NEW YORK, NY 10017 USA
语种	英语
资助项目	National Natural Science Foundation of China[51675253]
WOS研究方向	Engineering
WOS记录号	WOS:000426864600160
内容类型	会议论文
源URL	[http://119.78.100.223/handle/2XXMBERH/36214]
专题	兰州理工大学
通讯作者	Lian, Jin
作者单位	Lanzhou Univ Technol, Sch Mech & Elect Engn, Lanzhou, Gansu, Peoples R China
推荐引用方式 GB/T 7714	Lian, Jin,Zhao, Rongzhen. Fault Diagnosis Model Based on NRS and EEMD for Rolling-element Bearing[C]. 见:.