Gearbox compound fault diagnosis using CEEMDAN feature extraction and a dual-attention multi-scale BiLSTM model.
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| Title: | Gearbox compound fault diagnosis using CEEMDAN feature extraction and a dual-attention multi-scale BiLSTM model. |
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| Authors: | Wu, Lianxin1 906889823@qq.com, Sun, Xiaojie1 sxjlm2003@163.com |
| Source: | Journal of Vibroengineering. Jun2026, Vol. 28 Issue 4, p869-886. 18p. |
| Subjects: | Fault diagnosis, Feature extraction, Dimensional reduction algorithms, Artificial neural networks, Power transmission, Vibration (Mechanics) |
| Abstract: | As a core component of mechanical transmission systems, the gearbox's operating state directly determines equipment reliability and industrial production safety. In actual working conditions, a single fault can easily evolve into a complex fault mode with multiple coupled faults. Traditional diagnostic methods face challenges such as insufficient feature extraction and low fault mode discrimination. To address this issue, an intelligent diagnostic model is proposed that integrates adaptive noise complete set empirical mode decomposition (CEEMDAN) feature extraction, multi-scale convolution, and a dual attention mechanism. First, CEEMDAN is used to decompose the vibration signal at multiple scales. After effective IMF filtering, time-domain, frequency-domain, fault-specific, and coupled interactive features are extracted to form a multidimensional feature set. Then, adaptive principal component analysis (PCA) is used to reduce the dimensionality to obtain a low-redundancy feature set. Subsequently, a diagnostic model containing multi-scale convolution, a bidirectional long short-term memory network (BiLSTM), and dual attention branches is constructed, and an improved loss function is combined to enhance the ability to distinguish complex fault features. Experimental results based on the Beijing Jiaotong University bogie gearbox bench dataset verify the effectiveness and robustness of the proposed method under complex fault modes, providing a reliable technical solution for gearbox fault diagnosis in industrial scenarios. [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 |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 195026779 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Gearbox compound fault diagnosis using CEEMDAN feature extraction and a dual-attention multi-scale BiLSTM model. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wu%2C+Lianxin%22">Wu, Lianxin</searchLink><relatesTo>1</relatesTo><i> 906889823@qq.com</i><br /><searchLink fieldCode="AR" term="%22Sun%2C+Xiaojie%22">Sun, Xiaojie</searchLink><relatesTo>1</relatesTo><i> sxjlm2003@163.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Vibroengineering%22">Journal of Vibroengineering</searchLink>. Jun2026, Vol. 28 Issue 4, p869-886. 18p. – 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="%22Dimensional+reduction+algorithms%22">Dimensional reduction algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Power+transmission%22">Power transmission</searchLink><br /><searchLink fieldCode="DE" term="%22Vibration+%28Mechanics%29%22">Vibration (Mechanics)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: As a core component of mechanical transmission systems, the gearbox's operating state directly determines equipment reliability and industrial production safety. In actual working conditions, a single fault can easily evolve into a complex fault mode with multiple coupled faults. Traditional diagnostic methods face challenges such as insufficient feature extraction and low fault mode discrimination. To address this issue, an intelligent diagnostic model is proposed that integrates adaptive noise complete set empirical mode decomposition (CEEMDAN) feature extraction, multi-scale convolution, and a dual attention mechanism. First, CEEMDAN is used to decompose the vibration signal at multiple scales. After effective IMF filtering, time-domain, frequency-domain, fault-specific, and coupled interactive features are extracted to form a multidimensional feature set. Then, adaptive principal component analysis (PCA) is used to reduce the dimensionality to obtain a low-redundancy feature set. Subsequently, a diagnostic model containing multi-scale convolution, a bidirectional long short-term memory network (BiLSTM), and dual attention branches is constructed, and an improved loss function is combined to enhance the ability to distinguish complex fault features. Experimental results based on the Beijing Jiaotong University bogie gearbox bench dataset verify the effectiveness and robustness of the proposed method under complex fault modes, providing a reliable technical solution for gearbox fault diagnosis in industrial scenarios. [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.2026.26068 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 869 Subjects: – SubjectFull: Fault diagnosis Type: general – SubjectFull: Feature extraction Type: general – SubjectFull: Dimensional reduction algorithms Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Power transmission Type: general – SubjectFull: Vibration (Mechanics) Type: general Titles: – TitleFull: Gearbox compound fault diagnosis using CEEMDAN feature extraction and a dual-attention multi-scale BiLSTM model. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wu, Lianxin – PersonEntity: Name: NameFull: Sun, Xiaojie IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 13928716 Numbering: – Type: volume Value: 28 – Type: issue Value: 4 Titles: – TitleFull: Journal of Vibroengineering Type: main |
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