Multi-feature fusion for fault diagnosis of rotating machinery based on convolutional neural network

doi:10.1016/j.comcom.2021.04.016

HFCAS OpenIR

	Multi-feature fusion for fault diagnosis of rotating machinery based on convolutional neural network
	Liu, Shaoqing 1,2,3; Ji, Zhenshan1,2 ; Wang, Yong1,2 ; Zhang, Zuchao1,2 ; Xu, Zhanghou 1,2; Kan, Chaohao 4; Jin, Ke 4
	2021-05-01
发表期刊	COMPUTER COMMUNICATIONS
ISSN	0140-3664
通讯作者	Wang, Yong(wayong@ipp.ac.cn)
摘要	The fast and efficient fault diagnosis is the key to guarantee uninterrupted working of facilities, which is more frugal and trustworthy than scheduled upkeep. At present, data acquisition and fault diagnosis based on a variety of sensors have become an indispensable means for manufacturing enterprises. However, through the independent analysis of all kinds of sensor data, the traditional analysis method fails to make full use of the interrelationship between data sources. A new feature fusion approach that is based on Convolutional Neural Network (CNN) is put forward in this study for rotating machinery fault diagnosis. For multi-source data, some data sources are extracted with empirical features and others are extracted with hidden features. CNN is adopted to obtain the recessive features of complex signal waveform, such as acceleration, displacement, etc. The fusion of statistical features and recessive features is a new set of features and is input into Light Gradient Boosting Machine (LightGBM) model. The stator and rotor fault experiment is designed and implemented to verify the advantages of the proposed method. Compared with the traditional approaches, this method is 3% more accurate or at least 4 times faster than the traditional method under the same conditions.
关键词	Fault diagnosis Feature fusion Multi-feature Convolutional Neural Network (CNN) Light Gradient Boosting Machine (LightGBM)
DOI	10.1016/j.comcom.2021.04.016
关键词[WOS]	SELECTION
收录类别	SCI
语种	英语
资助项目	National Key R&D Program of China[2017YFE0300500] ; National Key R&D Program of China[2017YFE0300505] ; National MCF Energy R&D Program of China[2018YFE0302100]
项目资助者	National Key R&D Program of China ; National MCF Energy R&D Program of China
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:000648694200014
出版者	ELSEVIER
引用统计	被引频次：23[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.hfcas.ac.cn:8080/handle/334002/122274
专题	中国科学院合肥物质科学研究院
通讯作者	Wang, Yong
作者单位	1.Chinese Acad Sci, Inst Plasma Phys, Div Control & Comp Applicat, Hefei 230031, Peoples R China 2.Chinese Acad Sci, Hefei Inst Phys Sci, Hefei 230031, Peoples R China 3.Univ Sci & Technol China, Hefei 230026, Peoples R China 4.Hefei Univ Technol, Hefei 230009, Peoples R China
第一作者单位	中科院等离子体物理研究所
通讯作者单位	中科院等离子体物理研究所
推荐引用方式 GB/T 7714	Liu, Shaoqing,Ji, Zhenshan,Wang, Yong,et al. Multi-feature fusion for fault diagnosis of rotating machinery based on convolutional neural network[J]. COMPUTER COMMUNICATIONS,2021,173.
APA	Liu, Shaoqing.,Ji, Zhenshan.,Wang, Yong.,Zhang, Zuchao.,Xu, Zhanghou.,...&Jin, Ke.(2021).Multi-feature fusion for fault diagnosis of rotating machinery based on convolutional neural network.COMPUTER COMMUNICATIONS,173.
MLA	Liu, Shaoqing,et al."Multi-feature fusion for fault diagnosis of rotating machinery based on convolutional neural network".COMPUTER COMMUNICATIONS 173(2021).