Rolling bearing fault diagnosis by Markov transition field and multi-dimension convolutional neural network

doi:10.1088/1361-6501/ac87c4

CORC > 兰州理工大学 > 兰州理工大学 > 机电工程学院

	Rolling bearing fault diagnosis by Markov transition field and multi-dimension convolutional neural network
	Lei, Chunli 1,2; Xue, Linlin 2; Jiao, Mengxuan 2; Zhang, Huqiang 2; Shi, Jiashuo 2
刊名	Measurement Science and Technology
	2022-11-01
卷号	33 期号:11
关键词	Chemical activation Convolution Convolutional neural networks Fault detection Neural network models Roller bearings Activation functions Condition Convolutional neural network E-rectified linear unit activation function Faults diagnosis Linear units Markov transition field Multi dimensions Multi-dimension attention Transition fields
ISSN号	0957-0233
DOI	10.1088/1361-6501/ac87c4
英文摘要	Safe and reliable operation of mechanical equipment depends on timely and accurate fault diagnosis. When the actual working conditions are complex and variable and the available sample data set is small, recognition accuracy of the rolling bearing fault diagnosis model is low. To solve this problem, a novel method based on Markov transition field (MTF) and multi-dimension convolutional neural network (MDCNN) is proposed in this paper. Firstly, the original vibration signals are converted into two-dimensional images containing temporal correlation by MTF. Then, a neural network model is constructed by using multi-dimension attention and E-rectified linear units (E-Relu) activation function to fully extract fault feature information. Finally, the MTF images are input into the model and the data is normalized using the group normalization method. The MDCNN model is validated on two different data sets, and the results show that compared with other intelligent fault diagnosis methods, the MDCNN has higher fault diagnosis accuracy and stronger robustness under both variable working conditions and small sample data sets conditions. © 2022 IOP Publishing Ltd.
WOS研究方向	Engineering ; Instruments & Instrumentation
语种	英语
出版者	Institute of Physics
WOS记录号	WOS:000847567400001
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/159737]
专题	机电工程学院
作者单位	1.Key Laboratory of Digital Manufacturing Technology and Application, Ministry of Education, Lanzhou University of Technology, Lanzhou; 730050, China 2.School of Mechanical and Electronical Engineering, Lanzhou University of Technology, Lanzhou; 730050, China;
推荐引用方式 GB/T 7714	Lei, Chunli,Xue, Linlin,Jiao, Mengxuan,et al. Rolling bearing fault diagnosis by Markov transition field and multi-dimension convolutional neural network[J]. Measurement Science and Technology,2022,33(11).
APA	Lei, Chunli,Xue, Linlin,Jiao, Mengxuan,Zhang, Huqiang,&Shi, Jiashuo.(2022).Rolling bearing fault diagnosis by Markov transition field and multi-dimension convolutional neural network.Measurement Science and Technology,33(11).
MLA	Lei, Chunli,et al."Rolling bearing fault diagnosis by Markov transition field and multi-dimension convolutional neural network".Measurement Science and Technology 33.11(2022).