Lightweight rolling bearing fault diagnosis via dense connectivity and adaptive soft thresholding.
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| Title: | Lightweight rolling bearing fault diagnosis via dense connectivity and adaptive soft thresholding. |
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| Authors: | Wang, Yanqi1 yanqi_wang@foxmail.com, Zhang, Songlin1 149721096@qq.com, Ding, Ruming1 398029523@qq.com, Luo, Cheng1 1466899043@qq.com, Zhong, Letian1 2371725573@gmail.com |
| Source: | Journal of Vibroengineering. May2026, Vol. 28 Issue 3, p581-598. 18p. |
| Subjects: | Fault diagnosis, Thresholding algorithms, Noise, Machine learning, Artificial neural networks, Deep learning |
| Abstract: | Deep learning-based fault diagnosis of rolling bearings is frequently challenged by strong ambient noise in vibration signals and the high computational cost of deployable models. While deeper networks can enhance performance, they often lead to parameter redundancy and information loss in deep layers, hindering industrial application. To achieve a balance between noise robustness and model lightweightness, this paper proposes SE-SDCTNet, a novel architecture built upon Sparse Dense Compact Thresholding (SDCT) blocks and Squeeze-and-Excitation (SE) blocks. The SDCT blocks employ dense connections for efficient feature reuse, while incorporating sparsity constraints and an integrated soft-thresholding mechanism to actively suppress noise and reduce parameters. Subsequently, SE blocks adaptively recalibrate channel-wise features to compensate for potential information loss due to sparsity and to enhance discriminative power. Furthermore, dilated convolutions are embedded to preserve multi-scale contextual information throughout the network. Evaluated on the Case Western Reserve University (CWRU) bearing dataset, SE-SDCTNet demonstrates superior diagnostic accuracy (e.g., 93.1 % under severe 2 dB noise) and robustness across various signal-to-noise ratios, while containing only 0.32 million parameters, merely about 3 % of ResNet18. In summary, this work provides a lightweight, accurate, and robust solution that facilitates the transition of data-driven fault diagnosis from theoretical research to practical industrial deployment. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Vibroengineering is the property of Extrica 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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| Items | – Name: Title Label: Title Group: Ti Data: Lightweight rolling bearing fault diagnosis via dense connectivity and adaptive soft thresholding. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wang%2C+Yanqi%22">Wang, Yanqi</searchLink><relatesTo>1</relatesTo><i> yanqi_wang@foxmail.com</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Songlin%22">Zhang, Songlin</searchLink><relatesTo>1</relatesTo><i> 149721096@qq.com</i><br /><searchLink fieldCode="AR" term="%22Ding%2C+Ruming%22">Ding, Ruming</searchLink><relatesTo>1</relatesTo><i> 398029523@qq.com</i><br /><searchLink fieldCode="AR" term="%22Luo%2C+Cheng%22">Luo, Cheng</searchLink><relatesTo>1</relatesTo><i> 1466899043@qq.com</i><br /><searchLink fieldCode="AR" term="%22Zhong%2C+Letian%22">Zhong, Letian</searchLink><relatesTo>1</relatesTo><i> 2371725573@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Vibroengineering%22">Journal of Vibroengineering</searchLink>. May2026, Vol. 28 Issue 3, p581-598. 18p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Fault+diagnosis%22">Fault diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Thresholding+algorithms%22">Thresholding algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Noise%22">Noise</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Deep learning-based fault diagnosis of rolling bearings is frequently challenged by strong ambient noise in vibration signals and the high computational cost of deployable models. While deeper networks can enhance performance, they often lead to parameter redundancy and information loss in deep layers, hindering industrial application. To achieve a balance between noise robustness and model lightweightness, this paper proposes SE-SDCTNet, a novel architecture built upon Sparse Dense Compact Thresholding (SDCT) blocks and Squeeze-and-Excitation (SE) blocks. The SDCT blocks employ dense connections for efficient feature reuse, while incorporating sparsity constraints and an integrated soft-thresholding mechanism to actively suppress noise and reduce parameters. Subsequently, SE blocks adaptively recalibrate channel-wise features to compensate for potential information loss due to sparsity and to enhance discriminative power. Furthermore, dilated convolutions are embedded to preserve multi-scale contextual information throughout the network. Evaluated on the Case Western Reserve University (CWRU) bearing dataset, SE-SDCTNet demonstrates superior diagnostic accuracy (e.g., 93.1 % under severe 2 dB noise) and robustness across various signal-to-noise ratios, while containing only 0.32 million parameters, merely about 3 % of ResNet18. In summary, this work provides a lightweight, accurate, and robust solution that facilitates the transition of data-driven fault diagnosis from theoretical research to practical industrial deployment. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Vibroengineering is the property of Extrica 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: 10.21595/jve.2026.25314 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 581 Subjects: – SubjectFull: Fault diagnosis Type: general – SubjectFull: Thresholding algorithms Type: general – SubjectFull: Noise Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Deep learning Type: general Titles: – TitleFull: Lightweight rolling bearing fault diagnosis via dense connectivity and adaptive soft thresholding. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wang, Yanqi – PersonEntity: Name: NameFull: Zhang, Songlin – PersonEntity: Name: NameFull: Ding, Ruming – PersonEntity: Name: NameFull: Luo, Cheng – PersonEntity: Name: NameFull: Zhong, Letian IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 13928716 Numbering: – Type: volume Value: 28 – Type: issue Value: 3 Titles: – TitleFull: Journal of Vibroengineering Type: main |
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