Comparative analysis of deep learning algorithms for rolling element bearing fault classification under variable loads and speeds – Scientific Reports

1. Xu, F. et al. A Review of bearing failure modes, mechanisms and causes. Eng. Fail. Anal. 152, 107518 (2023). https://doi.org/10.1016/j.engfailanal.2023.107518

   Google Scholar 

2. Siddique, M. F., Saleem, F., Umar, M., Kim, C. H. & Kim, J. M. A Hybrid deep learning approach for bearing fault diagnosis using continuous wavelet transform and attention-enhanced spatiotemporal feature extraction. Sensors 25 (9), 2712–2712. (2025). https://doi.org/10.3390/s25092712

   Google Scholar 

3. Li, Y., Gu, X. & Wei, Y. A deep learning-based method for bearing fault diagnosis with few-shot learning. Sensors 24 (23), 7516. (2024). https://doi.org/10.3390/s24237516

   Google Scholar 

4. Hakim, M. et al. Bearing fault diagnosis using lightweight and robust one-dimensional convolution neural network in the frequency domain. Sensors 22 (15), 5793–5793. (2022). https://doi.org/10.3390/s22155793

   Google Scholar 

5. Zhang, Y. et al. Attention activation network for bearing fault diagnosis under various noise environments. Sci. Rep. 15 (1), 977 (2025). https://doi.org/10.1038/s41598-025-85275-w

   Google Scholar 

6. Chen, Y., Chen, Q. & Wang, R. Bearing fault diagnosis based on vibration envelope spectral characteristics. Appl. Sci. 15 (4), 2240. (2025). https://doi.org/10.3390/app15042240

   Google Scholar 

7. Fu, G., Wei, Q. & Yang, Y. Bearing fault diagnosis with parallel CNN and LSTM. Math. Biosci. Eng. 21 (2), 2385–2406. (2024). https://doi.org/10.3934/mbe.2024105

   Google Scholar 

8. Zhu, H., Sui, Z., Xu, J. & Lan, Y. Fault diagnosis of mechanical rolling bearings using a convolutional neural network–gated recurrent unit method with envelope analysis and adaptive mean filtering. Processes 12 (12), 2845–2845. (2024). https://doi.org/10.3390/pr12122845

   Google Scholar 

9. Sohaib, M., Kim, C. H. & Kim, J. M. A hybrid feature model and deep-learning-based bearing fault diagnosis. Sensors 17 (12), 2876 (2017). https://doi.org/10.3390/s17122876

   Google Scholar 

10. Kumar, K. K. & Mandava, S. Real-time bearing fault classification of induction motor using enhanced inception ResNet-V2. Appl. Artif. Intell. (2024). https://doi.org/10.1080/08839514.2024.2378270

11. Chennai Viswanathan, P. et al. Deep learning for enhanced fault diagnosis of monoblock centrifugal pumps: Spectrogram-based analysis. Machines 11 (9), 874. (2023). https://doi.org/10.3390/machines11090874

    Google Scholar 

12. Alqunun, K. et al. An efficient bearing fault detection strategy based on a hybrid machine learning technique. Sci. Rep. 15 (1), 18739 (2025). https://doi.org/10.1038/s41598-025-02439-4

    Google Scholar 

13. Shen, J., Chowdhury, J., Banerjee, S. & Terejanu, G. Machine fault classification using hamiltonian neural networks. arXiv:2301.02243v1 (2023).

14. Toma, R. N. et al. A Bearing Fault classification framework based on image encoding techniques and a convolutional neural network under different operating conditions. Sensors 22(13), 4881 (2022). https://doi.org/10.3390/s22134881

15. Tan, M. & Le, Q. V. EfficientNetV2: Smaller Models and Faster Training. arXiv:2104.00298v3 (2021).

16. Lokesha, M., Majumder, M., Ramachandran, K. & Raheem, K. Fault diagnosis in gear using wavelet envelope power spectrum. Int. J. Eng. Sci. Technol. 3 (8), 156–167 (2011). https://doi.org/10.4314/ijest.v3i8.13

    Google Scholar 

17. Pandiyan, M. & Babu, T. N. Implementation of MF block in CNN for advanced REB fault diagnosis. Sci. Rep. 15 (1), 18232 (2025). https://doi.org/10.1038/s41598-025-01780-y

    Google Scholar 

18. Christoph Bienefeld, F. M., Becker-Dombrowsky, E., Kirchner, E. & Shatri & Investigation of feature engineering methods for domain-knowledge-assisted bearing fault diagnosis. Entropy 25 (9), 1278–1278 (2023). https://doi.org/10.3390/e25091278

    Google Scholar 

19. Lundstrom, A. & Mattias, O. N. Factory-Based vibration data for bearing-fault detection. Data 8 (7), 115–115. (2023). https://doi.org/10.3390/data8070115

    Google Scholar 

20. Raj, K. K., Kumar, S., Kumar, R. R. & Andriollo, M. Enhanced Fault detection in bearings using machine learning and raw accelerometer data: a case study using the case western reserve university dataset. Information 15, 259–259. (2024). https://doi.org/10.3390/info15050259

    Google Scholar 

21. Yang, Z. et al. Hybrid CNN-BiLSTM-MHSA model for accurate fault diagnosis of rotor motor bearings. Mathematics 13 (3), 334–334. (2025). https://doi.org/10.3390/math13030334

    Google Scholar 

22. Siddique, M. F., Zaman, W., Umar, M., Kim, J. Y. & Kim, J. M. A hybrid deep learning framework for fault diagnosis in milling machines. Sensors 25 (18), 5866. https://doi.org/10.3390/s25185866 (2025)

    Google Scholar 

23. Zaman, W., Siddique, M. F. & Kim, J. M. Centrifugal pump fault detection with hybrid feature pool and deep learning. In 2023 20th International Bhurban Conference on Applied Sciences and Technology (IBCAST), Bhurban, Murree, Pakistan, 1–6 (2023). https://doi.org/10.1109/IBCAST59916.2023.10712967

24. Ullah, S., Siddique, M. F. & Kim, J. M. Multi-sensor observer-based residual learning with auto-permutation feature importance for fault diagnosis of multistage centrifugal pumps under variable pressures. Sci. Rep. 15, 45735. https://doi.org/10.1038/s41598-025-32726-z (2025).

    Google Scholar 

25. Muhammad Umar, M. F., Siddique, J. M. & Kim Burst-informed acoustic emission framework for explainable failure diagnosis in milling machines. Eng. Fail. Anal. 185, 1350–6307. https://doi.org/10.1016/j.engfailanal.2025.110373 (2026).

    Google Scholar 

26. Fallahy, S. & Rezazadeh, N. MARBLE-DA: Masonry analysis with robust, batch-normalised, label-free, explainable domain adaptation for crack detection. J. Build. Eng. 116, 2352–7102. https://doi.org/10.1016/j.jobe.2025.114673 (2025).

    Google Scholar 

27. Antoni, J. The spectral kurtosis: A useful tool for characterising non-stationary signals. Mech. Syst. Signal Process. 20 (2), 282–307 (2006). https://doi.org/10.1016/j.ymssp.2004.09.001

    Google Scholar 

28. Dosovitskiy, A. An image is worth 16×16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020).

Download references