A Multiscale Dilated Convolutional Network With Multihead Attention for Gearbox Intelligent Fault Diagnosis.
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| Title: | A Multiscale Dilated Convolutional Network With Multihead Attention for Gearbox Intelligent Fault Diagnosis. |
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| Authors: | Zhong, Shangkun1 (AUTHOR), Li, Guoqiang1 (AUTHOR) lgq@jmu.edu.cn, Zheng, Chengjie1 (AUTHOR), Liu, Wenwei2 (AUTHOR) liuwenwei_dzws@yeah.net, Cheng, Yiwei3 (AUTHOR), Wu, Defeng1 (AUTHOR), Wang, Huiqi (AUTHOR) wanghuiqi@cqu.edu.cn |
| Source: | Shock & Vibration. 6/30/2026, Vol. 2026, p1-12. 12p. |
| Subjects: | Fault diagnosis, Feature extraction, Convolutional neural networks, Condition-based maintenance, Receptive fields (Neurology) |
| Abstract: | The manual maintenance mode of gearboxes suffers from low efficiency and high subjectivity, making it difficult to meet the requirements of intelligent production. Therefore, achieving accurate condition identification and diagnosis of gearboxes is essential for further innovating intelligent maintenance of gearboxes. However, existing intelligent fault diagnosis methods for gearboxes suffer from insufficient mining of deep state features, resulting in poor effectiveness and generalization in practical applications. To solve the above challenges, this paper designed a new feature learning module that is named multiscale dilated multihead attention network (MDCN‐MHA). It can effectively expand the receptive field and enhance the ability to capture key fault features from gearbox condition data. Experimental verification and ablation studies show that the model with three multihead attention outperforms other comparison model variants in terms of accuracy and stability. Even under various noise levels, it still maintains high classification accuracy. [ABSTRACT FROM AUTHOR] |
| Copyright of Shock & Vibration is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 194973842 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Multiscale Dilated Convolutional Network With Multihead Attention for Gearbox Intelligent Fault Diagnosis. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhong%2C+Shangkun%22">Zhong, Shangkun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Guoqiang%22">Li, Guoqiang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> lgq@jmu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zheng%2C+Chengjie%22">Zheng, Chengjie</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Wenwei%22">Liu, Wenwei</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> liuwenwei_dzws@yeah.net</i><br /><searchLink fieldCode="AR" term="%22Cheng%2C+Yiwei%22">Cheng, Yiwei</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wu%2C+Defeng%22">Wu, Defeng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Huiqi%22">Wang, Huiqi</searchLink> (AUTHOR)<i> wanghuiqi@cqu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Shock+%26+Vibration%22">Shock & Vibration</searchLink>. 6/30/2026, Vol. 2026, p1-12. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Fault+diagnosis%22">Fault diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Condition-based+maintenance%22">Condition-based maintenance</searchLink><br /><searchLink fieldCode="DE" term="%22Receptive+fields+%28Neurology%29%22">Receptive fields (Neurology)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The manual maintenance mode of gearboxes suffers from low efficiency and high subjectivity, making it difficult to meet the requirements of intelligent production. Therefore, achieving accurate condition identification and diagnosis of gearboxes is essential for further innovating intelligent maintenance of gearboxes. However, existing intelligent fault diagnosis methods for gearboxes suffer from insufficient mining of deep state features, resulting in poor effectiveness and generalization in practical applications. To solve the above challenges, this paper designed a new feature learning module that is named multiscale dilated multihead attention network (MDCN‐MHA). It can effectively expand the receptive field and enhance the ability to capture key fault features from gearbox condition data. Experimental verification and ablation studies show that the model with three multihead attention outperforms other comparison model variants in terms of accuracy and stability. Even under various noise levels, it still maintains high classification accuracy. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Shock & Vibration is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1155/vib/6073130 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 1 Subjects: – SubjectFull: Fault diagnosis Type: general – SubjectFull: Feature extraction Type: general – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Condition-based maintenance Type: general – SubjectFull: Receptive fields (Neurology) Type: general Titles: – TitleFull: A Multiscale Dilated Convolutional Network With Multihead Attention for Gearbox Intelligent Fault Diagnosis. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhong, Shangkun – PersonEntity: Name: NameFull: Li, Guoqiang – PersonEntity: Name: NameFull: Zheng, Chengjie – PersonEntity: Name: NameFull: Liu, Wenwei – PersonEntity: Name: NameFull: Cheng, Yiwei – PersonEntity: Name: NameFull: Wu, Defeng – PersonEntity: Name: NameFull: Wang, Huiqi IsPartOfRelationships: – BibEntity: Dates: – D: 30 M: 06 Text: 6/30/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 10709622 Numbering: – Type: volume Value: 2026 Titles: – TitleFull: Shock & Vibration Type: main |
| ResultId | 1 |
