A remaining useful life prediction method based on time–frequency images of the mechanical vibration signals

doi:10.1016/j.measurement.2022.111782

	A remaining useful life prediction method based on time–frequency images of the mechanical vibration signals
	Du, Xianjun; Jia, Wenchao; Yu, Ping; Shi, Yaoke; Cheng, Shengyi
	2022-10
发表期刊	Measurement: Journal of the International Measurement Confederation
ISSN	0263-2241
卷号	202
摘要	As a key component of the rotating machines, rolling bearings are widely used in mechanical engineering, aerospace and other fields. The health condition is closely related to the safe operation of the equipment. Predicting the degradation trend and remaining useful life of rolling bearings can enable effective preventive maintenance of rotating machinery. Therefore, an attention mechanism based multiscale convolutional neural network prediction model is proposed in this paper. Firstly, the continuous wavelet transform (CWT) is used to transform the one-dimensional vibration signal collected by the sensor into a two-dimensional time–frequency spectral feature map. Secondly, the quadratic degradation function is selected to determine the health indices of the bearings. Thirdly, the multi-scale convolutional neural network (MSCNN) is employed to realize the deep feature extraction. The multi-scale fusion features are constructed by extracting different degradation features of the signal using convolutional kernels of different sizes, and the necessary degradation features extracted are further enhanced and non-essential features are suppressed through a convolutional attention mechanism. Finally, the proposed model is verified on the PRONOSTIA dataset and compared with other prediction methods. The results indicate that the proposed one achieves better performances than other algorithms with the lowest prediction error and the highest prediction score. It is verified that this method can effectively improve the prediction accuracy and generalization performance, which could provide a certain theoretical basis and for RUL prediction of bearings and other equipment. © 2022 Elsevier Ltd
关键词	Convolution Forecasting Preventive maintenance Rotating machinery Vibrations (mechanical) Wavelet transforms Attention mechanisms Continuous Wavelet Transform Convolutional neural network Degradation trend Feature map Remaining useful life predictions Rolling bearings Spectral feature Time frequency Time–frequency spectral feature map
DOI	10.1016/j.measurement.2022.111782
收录类别	EI ; SCIE
语种	英语
WOS研究方向	Engineering ; Instruments & Instrumentation
WOS类目	Engineering, Multidisciplinary ; Instruments & Instrumentation
WOS记录号	WOS:000860678300003
出版者	Elsevier B.V.
EI入藏号	20223612699438
EI主题词	Roller bearings
EI分类号	601.1 Mechanical Devices ; 601.2 Machine Components ; 716.1 Information Theory and Signal Processing ; 913.5 Maintenance ; 921.3 Mathematical Transformations ; 931.1 Mechanics
来源库	WOS
引用统计	被引频次：5[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/159751
专题	电气工程与信息工程学院
通讯作者	Du, Xianjun
作者单位	Lanzhou Univ Technol, Coll Elect & Informat Engn, Lanzhou 730050, Peoples R China
第一作者单位	电气工程与信息工程学院
通讯作者单位	电气工程与信息工程学院
第一作者的第一单位	电气工程与信息工程学院
推荐引用方式 GB/T 7714	Du, Xianjun,Jia, Wenchao,Yu, Ping,et al. A remaining useful life prediction method based on time–frequency images of the mechanical vibration signals[J]. Measurement: Journal of the International Measurement Confederation,2022,202.
APA	Du, Xianjun,Jia, Wenchao,Yu, Ping,Shi, Yaoke,&Cheng, Shengyi.(2022).A remaining useful life prediction method based on time–frequency images of the mechanical vibration signals.Measurement: Journal of the International Measurement Confederation,202.
MLA	Du, Xianjun,et al."A remaining useful life prediction method based on time–frequency images of the mechanical vibration signals".Measurement: Journal of the International Measurement Confederation 202(2022).