Academic Journal
Bearing Dynamics Identification with SINDy-Based Neural Network and Physics Model.
| Τίτλος: | Bearing Dynamics Identification with SINDy-Based Neural Network and Physics Model. |
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| Συγγραφείς: | Fang, Yu, Li, Zhaorong, Zhu, Liang, Wu, Zhen, Ping, Yan, Zhou, Kai |
| Πηγή: | Machines; Jun2026, Vol. 14 Issue 6, p620, 23p |
| Θεματικοί όροι: | Nonlinear dynamical systems, Mechanical vibration research, Model validation, Software libraries (Computer programming), Finite difference method, Rotor dynamics |
| Περίληψη: | Deep neural networks can fit nonlinear bearing vibration responses, but their learned parameters are difficult to relate to contact deformation, rolling element angular position, and other acceleration-generating mechanisms. To improve physical traceability in data-driven bearing dynamics identification, this study develops a physics-informed SINDy-NN with a mechanism-guided feature library. This paper presents a novel approach for constructing a physics-informed SINDy-NN (Sparse Identification of Nonlinear Dynamics-based Neural Network) and demonstrates its application in identifying bearing dynamics. A 5-DoF (five Degrees of Freedom) bearing dynamics model is built, and the primary components influencing the acceleration response are analyzed. This analysis forms the basis for defining a physics-explainable basis function library for the SINDy-NN. For comparison, widely used polynomial and Fourier libraries are also employed to evaluate modeling accuracy and convergence speed. Furthermore, to address the limited number of bearing data, virtual states are generated by applying multiple finite differences to the acceleration signal, expanding the dimensionality of the model and enabling the use of a Multi-Input–Multi-Output (MIMO) model in SINDy-NN. Finally, experimental data from the FEMTO bearing test bench are utilized for validation. The results demonstrate that the physics-informed SINDy-NN offers superior modeling efficiency, with sufficient accuracy and improved interpretability compared to general SINDy-NN. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: | Complementary Index |
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| Header | DbId: edb DbLabel: Complementary Index An: 194908192 RelevancyScore: 1082 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1082.4189453125 |
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| Items | – Name: Title Label: Title Group: Ti Data: Bearing Dynamics Identification with SINDy-Based Neural Network and Physics Model. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Fang%2C+Yu%22">Fang, Yu</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+Zhaorong%22">Li, Zhaorong</searchLink><br /><searchLink fieldCode="AR" term="%22Zhu%2C+Liang%22">Zhu, Liang</searchLink><br /><searchLink fieldCode="AR" term="%22Wu%2C+Zhen%22">Wu, Zhen</searchLink><br /><searchLink fieldCode="AR" term="%22Ping%2C+Yan%22">Ping, Yan</searchLink><br /><searchLink fieldCode="AR" term="%22Zhou%2C+Kai%22">Zhou, Kai</searchLink> – Name: TitleSource Label: Source Group: Src Data: Machines; Jun2026, Vol. 14 Issue 6, p620, 23p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Nonlinear+dynamical+systems%22">Nonlinear dynamical systems</searchLink><br /><searchLink fieldCode="DE" term="%22Mechanical+vibration+research%22">Mechanical vibration research</searchLink><br /><searchLink fieldCode="DE" term="%22Model+validation%22">Model validation</searchLink><br /><searchLink fieldCode="DE" term="%22Software+libraries+%28Computer+programming%29%22">Software libraries (Computer programming)</searchLink><br /><searchLink fieldCode="DE" term="%22Finite+difference+method%22">Finite difference method</searchLink><br /><searchLink fieldCode="DE" term="%22Rotor+dynamics%22">Rotor dynamics</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Deep neural networks can fit nonlinear bearing vibration responses, but their learned parameters are difficult to relate to contact deformation, rolling element angular position, and other acceleration-generating mechanisms. To improve physical traceability in data-driven bearing dynamics identification, this study develops a physics-informed SINDy-NN with a mechanism-guided feature library. This paper presents a novel approach for constructing a physics-informed SINDy-NN (Sparse Identification of Nonlinear Dynamics-based Neural Network) and demonstrates its application in identifying bearing dynamics. A 5-DoF (five Degrees of Freedom) bearing dynamics model is built, and the primary components influencing the acceleration response are analyzed. This analysis forms the basis for defining a physics-explainable basis function library for the SINDy-NN. For comparison, widely used polynomial and Fourier libraries are also employed to evaluate modeling accuracy and convergence speed. Furthermore, to address the limited number of bearing data, virtual states are generated by applying multiple finite differences to the acceleration signal, expanding the dimensionality of the model and enabling the use of a Multi-Input–Multi-Output (MIMO) model in SINDy-NN. Finally, experimental data from the FEMTO bearing test bench are utilized for validation. The results demonstrate that the physics-informed SINDy-NN offers superior modeling efficiency, with sufficient accuracy and improved interpretability compared to general SINDy-NN. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Machines is the property of MDPI 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: 10.3390/machines14060620 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 620 Subjects: – SubjectFull: Nonlinear dynamical systems Type: general – SubjectFull: Mechanical vibration research Type: general – SubjectFull: Model validation Type: general – SubjectFull: Software libraries (Computer programming) Type: general – SubjectFull: Finite difference method Type: general – SubjectFull: Rotor dynamics Type: general Titles: – TitleFull: Bearing Dynamics Identification with SINDy-Based Neural Network and Physics Model. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Fang, Yu – PersonEntity: Name: NameFull: Li, Zhaorong – PersonEntity: Name: NameFull: Zhu, Liang – PersonEntity: Name: NameFull: Wu, Zhen – PersonEntity: Name: NameFull: Ping, Yan – PersonEntity: Name: NameFull: Zhou, Kai IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20751702 Numbering: – Type: volume Value: 14 – Type: issue Value: 6 Titles: – TitleFull: Machines Type: main |
| ResultId | 1 |