Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Sep 4;20(9):e0331128.
doi: 10.1371/journal.pone.0331128. eCollection 2025.

Bearing fault diagnosis based on Kepler algorithm and attention mechanism

Yu Jie Guang  1 , Xiao Shun Gen  2 , Song Meng Meng  1   3 , Yu Wen Hui  4 , Fang Yan  5 , Ying He Jie  1
Affiliations

Bearing fault diagnosis based on Kepler algorithm and attention mechanism

Yu Jie Guang et al. PLoS One. .

Abstract

As a crucial component in rotating machinery, bearings are prone to varying degrees of damage in practical application scenarios. Therefore, studying the fault diagnosis of bearings is of great significance. This article proposes the Kepler algorithm to optimize the weights of neural networks and improve the diagnostic accuracy of the model. At the same time, combined with attention mechanisms, the model will focus on useful information, ignore useless information, and efficiently extract key features. Finally, using third-party bearing data and inputting it into the fault diagnosis model, it was verified that Kepler algorithm and attention mechanism can improve the diagnostic accuracy. Meanwhile, the algorithm proposed in this paper was compared with other algorithms to verify its feasibility and superiority.

PubMed Disclaimer

Conflict of interest statement

There is no conflict of interest in this paper.

Figures

Fig 1
Fig 1. Planets orbiting the Sun.
Fig 2
Fig 2. Exploration and development process of planets.
Fig 3
Fig 3. Kepler algorithm flowchart.
Fig 4
Fig 4. M-P neuron model and fully connected layer structure.
Fig 5
Fig 5. Process of One Dimensional Convolution Operation.
Fig 6
Fig 6. One dimensional max pooling process.
Fig 7
Fig 7. Attention mechanism model.
Fig 8
Fig 8. Fault diagnosis model based on Kepler algorithm and attention mechanism.
Fig 9
Fig 9. Experimental collection platform.
Fig 10
Fig 10. Damage to Inner.
Fig 11
Fig 11. Damage to Ring of Bearing the outer ring of the bearing.
Fig 12
Fig 12. Damaged Bearing Retainer.
Fig 13
Fig 13. Comparison of Kepler algorithm results.
Fig 14
Fig 14. Comparison of Attention Mechanism Results.
Fig 15
Fig 15. Comparison of F1-macro for four Models.
Fig 16
Fig 16. Test set prediction results of four algorithms.
Fig 17
Fig 17. Confusion Matrix.
Fig 18
Fig 18. t-SNE Visualization strictly simulated by Confusion Matrix.
See this image and copyright information in PMC

References

    1. Sun W e i f e n g. Design and implementation of rolling bearing vibration signal analysis and data processing system. Harbin Institute of Technology. 2023.
    1. Thorsen OV, Dalva M. Failure identification and analysis for high-voltage induction motors in the petrochemical industry. IEEE Trans on Ind Applicat. 1999;35(4):810–8. doi: 10.1109/28.777188 - DOI
    1. Report IC. Report of large motor reliability survey of industrial and commercial installations. Industry Applications IEEE Transactions on. 1987;ia–23(4):153–8.
    1. Glowacz A, Glowacz W, Glowacz Z, Kozik J. Early fault diagnosis of bearing and stator faults of the single-phase induction motor using acoustic signals. Measurement. 2018;113:1–9. doi: 10.1016/j.measurement.2017.08.036 - DOI
    1. Anand R, Rath A, Sahoo PK, Jain P, Panda G, Wang X, et al. Winograd Transform-Based Fast Detection of Heart Disease Using ECG Signals and Chest X-Ray Images. IEEE Access. 2025;13:57119–40. doi: 10.1109/access.2025.3555518 - DOI

LinkOut - more resources