Improved Alexnet Based Fault Diagnosis Method for Rolling Bearing Under Variable Conditions

doi:10.16450/j.cnki.issn.1004-6801.2020.03.007

	Improved Alexnet Based Fault Diagnosis Method for Rolling Bearing Under Variable Conditions
	Zhao, Xiaoqiang1,2,3 ; Zhang, Qingqing 1,2
	2020-06-01
发表期刊	Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis
ISSN	10046801
卷号	40 期号:3 页码:472-480
摘要	Rolling bearings in rotating machinery often work in the environment with variable loads and strong noise. Traditional fault diagnosis methods of rolling bearings are difficult to adaptively extract the favorable features under complex conditions, so a fault diagnosis method of rolling bearings with variable conditionsis proposedbased on improved AlexNet. Firstly, one-dimensional time-domain signals are translated into two-dimensional feature maps using transverse insert samples method to satisfy the requirements of the improved AlexNet input. Compared with the existing longitudinal insert samples method or two-dimensional spectrums method, the time series and correlation of vibration signals are preserved during feature extraction automatically. Secondly, the functional layer of AlexNet convolutional layer is improved and adjusted, andthe profitable characteristics for the state identification of rolling bearingscanbe automatically extracted via the convolution and sampling operations of improved AlexNet from the two-dimensional feature maps. Finally, the softmax cross entropy is considered as a loss function and Adam is used to realize the fault diagnosis of rolling bearings according to a small batch iterative optimization method. Compared the diagnosis effects with other methods for 12 kinds of states of different positions and damage degrees of rolling bearings under variable loads and strong noise, the results show that the proposed method has a higher accuracy of fault diagnosis of rolling bearing and its robustness is stronger. © 2020, Editorial Department of JVMD. All right reserved.
关键词	Convolution Failure analysis Fault detection Iterative methods Time domain analysis Complex condition Fault diagnosis method Iterative Optimization State identification Time-domain signal Two-dimensional features Two-dimensional spectrum Variable conditions
DOI	10.16450/j.cnki.issn.1004-6801.2020.03.007
收录类别	EI
语种	中文
出版者	Nanjing University of Aeronautics an Astronautics
EI入藏号	20203108986750
EI主题词	Roller bearings
EI分类号	601.2 Machine Components - 716.1 Information Theory and Signal Processing - 921 Mathematics - 921.6 Numerical Methods
来源库	Compendex
分类代码	601.2 Machine Components - 716.1 Information Theory and Signal Processing - 921 Mathematics - 921.6 Numerical Methods
引用统计	无
文献类型	期刊论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/115251
专题	电气工程与信息工程学院
作者单位	1.College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou; 730050, China; 2.Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou; 730050, China; 3.National Experimental Teaching Center of Electrical and Control Engineering, Lanzhou University of Technology, Lanzhou; 730050, China
第一作者单位	兰州理工大学
第一作者的第一单位	兰州理工大学
推荐引用方式 GB/T 7714	Zhao, Xiaoqiang,Zhang, Qingqing. Improved Alexnet Based Fault Diagnosis Method for Rolling Bearing Under Variable Conditions[J]. Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis,2020,40(3):472-480.
APA	Zhao, Xiaoqiang,&Zhang, Qingqing.(2020).Improved Alexnet Based Fault Diagnosis Method for Rolling Bearing Under Variable Conditions.Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis,40(3),472-480.
MLA	Zhao, Xiaoqiang,et al."Improved Alexnet Based Fault Diagnosis Method for Rolling Bearing Under Variable Conditions".Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis 40.3(2020):472-480.