Fault diagnosis based on SPBO-SDAE and transformer neural network for rotating machinery

doi:10.1016/j.measurement.2021.110545

	Fault diagnosis based on SPBO-SDAE and transformer neural network for rotating machinery
	Du, Xianjun1,2,3 ; Jia, Liangliang 1; Ul Haq, Izaz 1
	2022-01
发表期刊	MEASUREMENT
ISSN	0263-2241
卷号	188
摘要	Fault diagnosis for rotating machinery requires both high diagnosis accuracy and time efficiency. A rotating machinery fault diagnosis method based on intelligent feature self-extraction and transformer neural network is proposed. Firstly, the proposed method employs the student psychology based optimization (SPBO) algorithm to adaptively select hyper parameters, including the number of hidden layer nodes, sparsity coefficient and input data zeroing ratio, of the denoising auto encoder (DAE) network to determine the optimal structure of the stacked denoising auto encoders (SDAE) network. Secondly, the optimized SPBO-SDAE network is used to extract features from high-dimensional original data layer by layer. On this basis, the weight parameters of self-extracted features of SPBO-SDAE network are optimized through the self-attention mechanism of transformer deep neural network. The target features are retained, and the redundant features are filtered. Finally, in order to further validate the performance of the proposed model in the complex conditions, by adding Gaussian noise to the original data, the diagnosis performance of the proposed method is verified through four open data sets. The simulation results indicate that compared with the existing common shallow learning and deep learning methods, the proposed method has great advantages in generalization performance, fault diagnosis accuracy and time efficiency.
关键词	Fault diagnosis Rotating machinery Hyper parameter optimization Feature self-extraction Transformer neural network Self attention mechanism
DOI	10.1016/j.measurement.2021.110545
收录类别	SCIE ; EI
语种	英语
WOS研究方向	Engineering ; Instruments & Instrumentation
WOS类目	Engineering, Multidisciplinary ; Instruments & Instrumentation
WOS记录号	WOS:000742857300002
出版者	ELSEVIER SCI LTD
EI入藏号	20215011326240
EI主题词	Failure analysis
EI分类号	461.4 Ergonomics and Human Factors Engineering ; 601.1 Mechanical Devices ; 716.1 Information Theory and Signal Processing ; 723.2 Data Processing and Image Processing ; 802.3 Chemical Operations ; 913.1 Production Engineering ; 921.5 Optimization Techniques
来源库	WOS
引用统计	被引频次：13[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/154900
专题	电气工程与信息工程学院
通讯作者	Du, Xianjun
作者单位	1.Lanzhou Univ Technol, Coll Elect & Informat Engn, Lanzhou 730050, Peoples R China; 2.Lanzhou Univ Technol, Key Lab Gansu Adv Control Ind Proc, Lanzhou 730050, Peoples R China; 3.Lanzhou Univ Technol, Natl Demonstrat Ctr Expt Elect & Control Engn Edu, Lanzhou 730050, Peoples R China
第一作者单位	电气工程与信息工程学院; 兰州理工大学
通讯作者单位	电气工程与信息工程学院; 兰州理工大学
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, Liangliang,Ul Haq, Izaz. Fault diagnosis based on SPBO-SDAE and transformer neural network for rotating machinery[J]. MEASUREMENT,2022,188.
APA	Du, Xianjun,Jia, Liangliang,&Ul Haq, Izaz.(2022).Fault diagnosis based on SPBO-SDAE and transformer neural network for rotating machinery.MEASUREMENT,188.
MLA	Du, Xianjun,et al."Fault diagnosis based on SPBO-SDAE and transformer neural network for rotating machinery".MEASUREMENT 188(2022).