Average Descent Rate Singular Value Decomposition and Two-Dimensional Residual Neural Network for Fault Diagnosis of Rotating Machinery

doi:10.1109/TIM.2022.3170973

	Average Descent Rate Singular Value Decomposition and Two-Dimensional Residual Neural Network for Fault Diagnosis of Rotating Machinery
	Liang, Haopeng 2; Cao, Jie 2; Zhao, Xiaoqiang 1
刊名	IEEE Transactions on Instrumentation and Measurement
	2022
卷号	71
关键词	Deep neural networks Failure analysis Fault detection Feature extraction Gears Rotating machinery Average descent rate singular value decomposition Convolutional neural network Deep learning Faults diagnosis Features extraction Gramian angular difference field Gramians Neural-networks Noise measurements Two-dimensional Two-dimensional residual neural network Vibration
ISSN号	0018-9456
DOI	10.1109/TIM.2022.3170973
英文摘要	Fault diagnosis of rotating machinery is difficult under the strong noisy environment. Although singular value decomposition (SVD) can remove noise from vibration signals, the singular value threshold is normally determined by expert experience. To solve this problem, a fault diagnosis method based on average descent rate (ADR)-SVD and two-dimensional residual neural network (Resnet) is proposed. First, ADR-SVD uses the ADR index to construct the singular value descent rate difference spectrum and uses the maximum value of the spectrum as the singular value threshold. The noise reduction process of ADR-SVD requires little expert experience. Then, in order to adaptively identify the fault features of the signals, we introduce Gramian angular difference field (GADF), which can transform the one-dimensional signals into two-dimensional images and preserve the temporal correlation of the one-dimensional signals. Finally, we construct a two-dimensional Resnet to learn image features and identify fault types. The proposed method is tested on Case Western Reserve University (CWRU) bearing dataset, Driveline Dynamic Simulator (DDS) gearbox dataset, and University of Connecticut (UoC) gearbox dataset under the strong noisy environment, which achieves the accuracies of 98.00%, 99.00%, and 98.88%, respectively. The accuracies of other deep learning methods and singular value difference spectrum method are below 95%. The comparisons show that the proposed method has better noise reduction effect and can diagnose the fault type more accurately. © 1963-2012 IEEE.
语种	英语
出版者	Institute of Electrical and Electronics Engineers Inc.
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/158436]
专题	电气工程与信息工程学院
作者单位	1.Lanzhou University of Technology, School of Electrical Engineering and Information Engineering, Gansu; 730050, China 2.Lanzhou University of Technology, School of Computer and Communication, Gansu; 730050, China;
推荐引用方式 GB/T 7714	Liang, Haopeng,Cao, Jie,Zhao, Xiaoqiang. Average Descent Rate Singular Value Decomposition and Two-Dimensional Residual Neural Network for Fault Diagnosis of Rotating Machinery[J]. IEEE Transactions on Instrumentation and Measurement,2022,71.
APA	Liang, Haopeng,Cao, Jie,&Zhao, Xiaoqiang.(2022).Average Descent Rate Singular Value Decomposition and Two-Dimensional Residual Neural Network for Fault Diagnosis of Rotating Machinery.IEEE Transactions on Instrumentation and Measurement,71.
MLA	Liang, Haopeng,et al."Average Descent Rate Singular Value Decomposition and Two-Dimensional Residual Neural Network for Fault Diagnosis of Rotating Machinery".IEEE Transactions on Instrumentation and Measurement 71(2022).