Application of EWT AR model and FCM clustering in rolling bearing fault diagnosis

doi:10.1109/PHM.2017.8079270

CORC > 兰州理工大学 > 兰州理工大学 > 机电工程学院

	Application of EWT AR model and FCM clustering in rolling bearing fault diagnosis
	Li, Jipu; Zhao, Rongzhen; Deng, Linfeng
	2017-10-20
会议日期	July 9, 2017 - July 12, 2017
会议地点	Harbin, China
关键词	Failure analysis Fault detection Frequency modulation Roller bearings Vibration analysis Wavelet transforms AR models Auto regressive models Fault diagnosis method Fault identifications Fuzzy C mean clustering Fuzzy C mean clustering (FCM) High dimensional feature Locality preserving projections
DOI	10.1109/PHM.2017.8079270
英文摘要	A fault diagnosis method is proposed, which is based on Empirical Wavelet Transform (EWT), Auto-Regressive (AR) model and Fuzzy C-Mean clustering (FCM) clustering algorithm, in order to solve the problem of fault category is difficult to identify of rolling bearing fault signal. In this method, the original signal of the rolling bearing is decomposed by the EWT, and several AM-FM components are obtained. The AR model is established for each AM-FM component, and the original feature subset is constructed. Then, through the correlation analysis, the four AM-FM components are extremely correlated with the original vibration signal are selected and their AR models are established. Construction of high-dimensional feature subsets based on the auto-regressive parameters of AR model. Finally, using the Locality Preserving Projection (LPP) algorithm to reduce the dimension and enter the low-dimensional feature subset to the FCM clustering, in order to achieve fault diagnosis of bearings. Experiments show that the fault identification method which is proposed in this paper has certain advantages and the fault recognition effect is better. © 2017 IEEE.
会议录	2017 Prognostics and System Health Management Conference, PHM-Harbin 2017 - Proceedings
会议录出版者	Institute of Electrical and Electronics Engineers Inc., United States
语种	英语
内容类型	会议论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/117990]
专题	机电工程学院
作者单位	School of Mechanical and Electronic Engineering, Lanzhou University of Technology, Lanzhou, China
推荐引用方式 GB/T 7714	Li, Jipu,Zhao, Rongzhen,Deng, Linfeng. Application of EWT AR model and FCM clustering in rolling bearing fault diagnosis[C]. 见:. Harbin, China. July 9, 2017 - July 12, 2017.