Health Condition Identification of Rolling Element Bearing Based on Gradient of Features Matrix and MDDCs-MRSVD

doi:10.1109/TIM.2022.3190062

CORC > 兰州理工大学 > 兰州理工大学 > 机电工程学院

	Health Condition Identification of Rolling Element Bearing Based on Gradient of Features Matrix and MDDCs-MRSVD
	Meng, Jiadong 1; Yan, Changfeng 1; Wang, Zonggang 2; Wen, Tao 3; Chen, Guangyi 1; Wu, Lixiao 1
刊名	IEEE Transactions on Instrumentation and Measurement
	2022
卷号	71 页码:1-1
关键词	Condition based maintenance Condition monitoring Health Interactive computer systems Roller bearings Feature matrices Gradient Health condition Initial faults Maximal difference of detail component in multi-resolution singular value decomposition algorithm Monitoring indicators Real Time system Rolling Element Bearing Singular value decomposition algorithms Vibration
ISSN号	0018-9456
DOI	10.1109/TIM.2022.3190062
英文摘要	Bearing is a key component in rotary machines, and the performance of the rotary machines mostly depends on the bearing health condition. In order to improve the safety and maintenance plan of the product based on the bearing condition, a monitoring indicator is constructed to identify the health condition of bearings in real time. Firstly, the vibration signal is processed by the proposed Maximal Difference of the Detail Components in Multi-Resolution Singular Value Decomposition (MDDCs-MRSVD) algorithm. Secondly, the features matrix is constructed by selected features to reflect the health condition of bearings. Then, the gradient standard deviation of each sampling time is obtained by the gradient in the amplitude direction of the features matrix. Finally, a monitoring indicator can be constructed to identify healthy stages of bearing. The proposed methods are verified via the tested datasets provided by Intelligent Maintenance Systems, and Xi'an Jiaotong University and the Changxing Sumyoung Technology Co., Ltd. (XJTU-SY). The results indicate that the proposed method is efficient and accurate to monitor and identify the health stages of bearing in real time. IEEE
WOS研究方向	Engineering ; Instruments & Instrumentation
语种	英语
出版者	Institute of Electrical and Electronics Engineers Inc.
WOS记录号	WOS:000846867100014
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/159389]
专题	机电工程学院
作者单位	1.School of Mechanical Electronical Engineering, Lanzhou University of Technology, Lanzhou, China; 2.College of Physics Electromechanical Engineering, Hexi University, Zhangye, China; 3.Gansu Computing Center, Lanzhou, China
推荐引用方式 GB/T 7714	Meng, Jiadong,Yan, Changfeng,Wang, Zonggang,et al. Health Condition Identification of Rolling Element Bearing Based on Gradient of Features Matrix and MDDCs-MRSVD[J]. IEEE Transactions on Instrumentation and Measurement,2022,71:1-1.
APA	Meng, Jiadong,Yan, Changfeng,Wang, Zonggang,Wen, Tao,Chen, Guangyi,&Wu, Lixiao.(2022).Health Condition Identification of Rolling Element Bearing Based on Gradient of Features Matrix and MDDCs-MRSVD.IEEE Transactions on Instrumentation and Measurement,71,1-1.
MLA	Meng, Jiadong,et al."Health Condition Identification of Rolling Element Bearing Based on Gradient of Features Matrix and MDDCs-MRSVD".IEEE Transactions on Instrumentation and Measurement 71(2022):1-1.