Abstract
This paper presents a comparative study of deep learning methods used to classify roller bearing faults. We obtained experimental vibration data by conducting controlled tests on roller bearings with various faults under different load conditions to simulate real-world industrial conditions. The methodology first evaluates four different time-frequency techniques-STFT, CWT, WPT and CQ-NSGT, establishing CQ-NSGT as the superior method for feature visualization. A novel hybrid transfer learning architecture that integrates pre-trained backbones (EfficientNetB0, MobileNetV2, InceptionV3) with custom residual blocks is proposed. These CNNs are compared to a baseline CNN and a Vision Transformer (ViT). Their performance was rigorously evaluated using fivefold cross-validation and tested against additive white Gaussian noise at Signal-to-Noise Ratios (SNR) of 1 dB, 3 dB, and 5 dB. The EfficientNetB0 hybrid model was able to reach the highest baseline accuracy of 99.83% while also exhibiting excellent robustness-maintaining 98.8% accuracy even at 1 dB SNR. MobileNetV2 is the most computationally efficient model with a training time of only 121.4 s and 0.50 GFLOPS making it perfect for edge deployment. ViT has potential but is less noise stable and lacks the inductive bias of CNNs which is important for this particular application. These results can be used as a guide for trading off accuracy of diagnosis and computational cost in industrial predictive maintenance.
Data availability
The dataset created and handled during the current research is not available publicly due to the limitations set by the institution. Aimed to be shared by the corresponding author only when a reasonable request is made and subject to institutional approval.
References
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
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
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
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
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
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
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
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
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
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
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
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
Shen, J., Chowdhury, J., Banerjee, S. & Terejanu, G. Machine fault classification using hamiltonian neural networks. arXiv:2301.02243v1 (2023).
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
Tan, M. & Le, Q. V. EfficientNetV2: Smaller Models and Faster Training. arXiv:2104.00298v3 (2021).
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
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
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
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
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
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
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)
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
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).
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).
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).
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
Dosovitskiy, A. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020).
Acknowledgements
The authors would like to thank Vellore Institute of Technology, Vellore for providing support and encouragements to complete this research work.
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Open access funding provided by Vellore Institute of Technology. This work was supported and funded by the Vellore Institute of Technology, Vellore.
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Pratyush Vishal—Simulation, Rohith Nair—Investigation, Jonathan Jacob—literature, Narendiranath Babu T—conceptualization and analysis, Pandiyan M—Methodology.
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Vishal, P., Nair, R., Jacob, J. et al. Comparative analysis of deep learning algorithms for rolling element bearing fault classification under variable loads and speeds. Sci Rep (2026). https://doi.org/10.1038/s41598-026-42592-y
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DOI: https://doi.org/10.1038/s41598-026-42592-y
