CASIA OpenIR  > 综合信息系统研究中心  > 工业智能技术与系统
Cross-Level fusion for rotating machinery fault diagnosis under compound variable working conditions
Wang, Sihan1; Wang, Dazhi1; Kong, Deshan1; Li, Wenhui1; Wang, Huanjie2,4; Pecht, Michael3
Source PublicationMEASUREMENT
ISSN0263-2241
2022-08-01
Volume199Pages:7
Corresponding AuthorWang, Dazhi(wdz_neu2021@163.com)
AbstractRotating machinery fault diagnosis based on deep learning has been successfully applied in modern industrial equipment. However, many existing types of research suffer from two significant deficiencies. First, most deep neural networks are based on a single or same kind of similarity measurement method, which cannot fully exploit the data to extract different levels of feature information. Second, most intelligent fault diagnosis methods can only partially solve the data sparsity and domain shift problems caused by small samples, noise, variable working conditions or compound faults. The model's performance will degenerate rapidly when the above problems occur simultaneously. To address this problem, this paper develops a cross-level fusion neural network method that extracts abundant information on features by calculating spatial-level, channel-level, and second-order statistical information and adaptively fusing the three levels to obtain the final relationship score. First, the signal is input into the embedding module through a Fast Fourier Transform to obtain the feature embedding of the onedimensional sequence signal. Then, the cross-level metrics learning module calculates the similarity of query sets and support sets at different levels. Finally, the similarities of different levels are fused through the adaptive fusion module to output the final relationship score. The bearing fault diagnosis experiments in the compound variable condition scenario show that the proposed method improves at least 78.53% compared to the traditional deep learning method, at least 3.22% and at most 35.52% compared to multiple few-shot learning methods. In addition, the ablation test analyzes the contribution of different level measurement methods to the model, and the maximum difference between them will reach 32.49%. In summary, the cross-level fusion method can effectively alleviate the data sparsity and domain shift problems.
KeywordIntelligent fault diagnosis Few-shot learning Anti-noise Compound faults Working condition variation
DOI10.1016/j.measurement.2022.111455
WOS KeywordNEURAL-NETWORK ; BEARINGS
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[52077027] ; Department of Science and Technology of Liaoning province[2020020304-JH1/101]
Funding OrganizationNational Natural Science Foundation of China ; Department of Science and Technology of Liaoning province
WOS Research AreaEngineering ; Instruments & Instrumentation
WOS SubjectEngineering, Multidisciplinary ; Instruments & Instrumentation
WOS IDWOS:000817179900005
PublisherELSEVIER SCI LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/49150
Collection综合信息系统研究中心_工业智能技术与系统
Corresponding AuthorWang, Dazhi
Affiliation1.Northeastern Univ, Sch Informat Sci & Engn, Shenyang 110819, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
3.Univ Maryland, Ctr Adv Life Cycle Engn, College Pk, MD 20742 USA
4.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Wang, Sihan,Wang, Dazhi,Kong, Deshan,et al. Cross-Level fusion for rotating machinery fault diagnosis under compound variable working conditions[J]. MEASUREMENT,2022,199:7.
APA Wang, Sihan,Wang, Dazhi,Kong, Deshan,Li, Wenhui,Wang, Huanjie,&Pecht, Michael.(2022).Cross-Level fusion for rotating machinery fault diagnosis under compound variable working conditions.MEASUREMENT,199,7.
MLA Wang, Sihan,et al."Cross-Level fusion for rotating machinery fault diagnosis under compound variable working conditions".MEASUREMENT 199(2022):7.
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