Intelligent fault diagnosis of train bearing based on ISTOA-VMD and SE-WDCNN

doi:10.1177/10775463231196351

CORC > 海洋研究所 > 中国科学院海洋研究所

	Intelligent fault diagnosis of train bearing based on ISTOA-VMD and SE-WDCNN
	He, Deqiang 2,3; Zou, Xueyan 2; Jin, Zhenzhen 2; Yan, Jingren 2; Ren, Chonghui 1; Zhou, Jixu 4
刊名	JOURNAL OF VIBRATION AND CONTROL
	2023-08-17
页码	12
关键词	intelligent fault diagnosis train bearing improved sooty tern optimization algorithm variational mode decomposition deep convolutional neural network
ISSN号	1077-5463
DOI	10.1177/10775463231196351
通讯作者	He, Deqiang(hdqianglqy@126.com)
英文摘要	Bearing plays a significant role in the transmission of traction forces and safe operation of train. Affected by the actual operating conditions of the train, it is of great significance to ensure the accurate diagnosis and classification of train bearing faults under strong noise background. An intelligent bearing fault diagnosis method based on the improved sooty tern optimization algorithm to optimize the variational mode decomposition (ISTOA-VMD) and the Squeeze-and-Excitation deep convolutional neural network with wide first-layer kernels (SE-WDCNN) is proposed. Firstly, an improved sooty tern optimization (ISTOA) is proposed by introducing the nonlinear convergence strategy and dynamic weight strategy, and the parameters of VMD are optimized by ISTOA. Furthermore, the VMD combined with sample entropy is used to reconstruct and denoise the signal. Finally, SE-WDCNN is proposed by fusing Squeeze-and-Excitation block, and the reconstructed signal is input into SE-WDCNN for automatic feature extraction and fault recognition. The experimental results show that the proposed method has significant effects on fault diagnosis tasks in different noise environments.
资助项目	National Natural Science Foundation of China[U22A2053] ; Major Project of Science and Technology of Guangxi Province of China[Guike AA20302010] ; Guangxi Manufacturing Systems and Advanced Manufacturing Technology Key Laboratory Director Fund[22-050-44-S015] ; Shandong Provincial Natural Science Foundation[ZR2020QF056] ; Innovation Project of Guangxi Graduate Education[YCSW2023086]
WOS关键词	OPTIMIZATION
WOS研究方向	Acoustics ; Engineering ; Mechanics
语种	英语
出版者	SAGE PUBLICATIONS LTD
WOS记录号	WOS:001062084800001
内容类型	期刊论文
源URL	[http://ir.qdio.ac.cn/handle/337002/181841]
专题	中国科学院海洋研究所
通讯作者	He, Deqiang
作者单位	1.Nanning Rail Transit Co Ltd, Nanning, Peoples R China 2.Guangxi Univ, Sch Mech Engn, Guangxi Key Lab Mfg Syst & Adv Mfg Technol, Nanning, Peoples R China 3.Guangxi Univ, Sch Mech Engn, 100 Daxue East Rd, Nanning 530004, Guangxi, Peoples R China 4.Chinese Acad Sci, Inst Oceanol, Qingdao, Peoples R China
推荐引用方式 GB/T 7714	He, Deqiang,Zou, Xueyan,Jin, Zhenzhen,et al. Intelligent fault diagnosis of train bearing based on ISTOA-VMD and SE-WDCNN[J]. JOURNAL OF VIBRATION AND CONTROL,2023:12.
APA	He, Deqiang,Zou, Xueyan,Jin, Zhenzhen,Yan, Jingren,Ren, Chonghui,&Zhou, Jixu.(2023).Intelligent fault diagnosis of train bearing based on ISTOA-VMD and SE-WDCNN.JOURNAL OF VIBRATION AND CONTROL,12.
MLA	He, Deqiang,et al."Intelligent fault diagnosis of train bearing based on ISTOA-VMD and SE-WDCNN".JOURNAL OF VIBRATION AND CONTROL (2023):12.