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Noise in Rolling Bearing Fault Diagnosis: From Characterization to Robust Solutions

Jiajun Liu, Baishun Su
Vol. 8, No. 1, pp. 14-28, 2026 DOI: 10.54097/mctewz43 July 28, 2026
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Abstract.Rolling bearing fault diagnosis is essential for the reliability and safety of rotating machinery, yet its effectiveness in industrial practice is fundamentally challenged by noise. This narrative review argues that noise in bearing fault diagnosis should not be treated as a single additive disturbance, but rather as a collection of distinct phenomena — environmental and background signal noise, sensor and acquisition noise, operating-condition-induced domain shift, label noise, and compound noise scenarios — each affecting different stages of the diagnostic pipeline and demanding differentiated strategies. This review develops a noise taxonomy for rolling bearing fault diagnosis and systematically examines how each noise type degrades signal processing, feature extraction, and diagnostic decision-making. It surveys denoising strategies, noise-robust feature engineering approaches, and noise-aware diagnostic models through this noise-type lens, identifying which strategies are appropriate for which noise conditions. A standardized evaluation and benchmarking protocol is proposed to address the inconsistency that currently prevents meaningful comparison of noise-robustness claims across studies. The central synthetic contribution is a noise–method matching matrix that maps each noise category to appropriate combinations of denoising, feature, model, and evaluation strategies, providing a structured framework for method selection and future research design. The review concludes that robust bearing fault diagnosis cannot be achieved through a single universal model. The more realistic path forward requires noise-type-aware strategies, standardized multi-noise evaluation, and methods co-designed for industrial deployment constraints.
Keywords. Rolling bearing fault diagnosis noise robustness noise taxonomy denoising domain shift label noise benchmarking
APA Citation Jiajun Liu, Baishun Su (2026). Noise in Rolling Bearing Fault Diagnosis: From Characterization to Robust Solutions. Journal of Computer Science and Artificial Intelligence, 8(1), 14-28. https://doi.org/10.54097/mctewz43

References

  1. Lei, Y., Yang, B., Jiang, X., Jia, F., Li, N., & Nandi, A. K. (2020). Applications of machine learning to machine fault diagnosis: A review and roadmap. Mechanical Systems and Signal Processing, 138, 106587. https://doi.org/10.1016/j.ymssp.2019.106587
  2. Zhang, S., Zhang, S., Wang, B., & Habetler, T. G. (2020). Deep learning algorithms for bearing fault diagnostics—A comprehensive review. IEEE Access, 8, 29857–29881. https://doi.org/10.1109/ACCESS.2020.2972859
  3. Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., & Gao, R. X. (2019). Deep learning and its applications to machine health monitoring. Mechanical Systems and Signal Processing, 115, 213–237. https://doi.org/10.1016/j.ymssp.2018.05.050
  4. Pancaldi, F., Dibiase, L., & Cocconcelli, M. (2023). Impact of noise model on the performance of algorithms for fault diagnosis in rolling bearings. Mechanical Systems and Signal Processing, 188, 109975. https://doi.org/10.1016/j.ymssp.2022.109975
  5. Hendriks, J., Dumond, P., & Knox, D. A. (2022). Towards better benchmarking using the CWRU bearing fault dataset. Mechanical Systems and Signal Processing, 169, 108732. https://doi.org/10.1016/j.ymssp.2021.108732
  6. Bao, Z., Liu, C., Yang, H., Zhang, J., & Li, Y. (2026). Deep learning-enabled intelligent diagnosis of bearing faults in complex environments: A comprehensive review. Engineering Applications of Artificial Intelligence, 163, 113068. https://doi.org/10.1016/j.engappai.2025.113068
  7. Chen, M., Zheng, J., Pei, H., Yang, L., Zhang, Q., Han, Q., & Luo, H. (2026). Advances and prospects in noise-robust fault diagnosis for rotating machinery: From mechanism-based approaches to data-driven models. Measurement Science and Technology, 37(19), 192002. https://doi.org/10.1088/1361-6501/ae65b7
  8. Hebda-Sobkowicz, J., Zimroz, R., Pitera, M., & Wyłomańska, A. (2020). Selection of the informative frequency band in a bearing fault diagnosis in the presence of non-Gaussian noise—Comparison of recently developed methods. Mechanical Systems and Signal Processing, 145, 106971. https://doi.org/10.1016/j.ymssp.2020.106971
  9. Sun, H., Gao, S., Ma, S., & Lin, S. (2022). A fault mechanism-based model for bearing fault diagnosis under non-stationary conditions without target condition samples. Measurement, 199, 111499. https://doi.org/10.1016/j.measurement.2022.111499
  10. Ma, Y., Yang, J., & Li, L. (2023). Meta Bi-classifier Gradient Discrepancy for noisy and universal domain adaptation in intelligent fault diagnosis. Knowledge-Based Systems, 276, 110735. https://doi.org/10.1016/j.knosys.2023.110735
  11. Schmidt, S., Wilke, D. N., & Gryllias, K. C. (2025). Generalised envelope spectrum-based signal-to-noise objectives: Formulation, optimisation and application for gear fault detection under time-varying speed conditions. Mechanical Systems and Signal Processing, 224, 111974. https://doi.org/10.1016/j.ymssp.2024.111974
  12. Liu, T., Wei, Z., Hu, L., Xie, X., Dong, X., Noman, K., & Li, Y. (2025). A resonance demodulation method based on amplitude Z-score of envelope spectrum for weak bearing fault detection. Mechanical Systems and Signal Processing, 238, 113265. https://doi.org/10.1016/j.ymssp.2025.113265
  13. Xu, H., & Zhou, S. (2025). Maximum L-Kurtosis deconvolution and frequency-domain filtering algorithm for bearing fault diagnosis. Mechanical Systems and Signal Processing, 223, 111916. https://doi.org/10.1016/j.ymssp.2024.111916
  14. Miao, Y., Li, C., Shi, H., & Han, T. (2023). Deep network-based maximum correlated kurtosis deconvolution: A novel deep deconvolution for bearing fault diagnosis. Mechanical Systems and Signal Processing, 189, 110110. https://doi.org/10.1016/j.ymssp.2023.110110
  15. Shen, J., Wang, Z., Wang, Y., Zhu, H., Zhang, L., & Tang, Y. (2025). AGWO-PSO-VMD-TEFCG-AlexNet bearing fault diagnosis method under strong noise. Measurement, 242, 116259. https://doi.org/10.1016/j.measurement.2024.116259
  16. Liao, J.-X., He, C., Li, J., Sun, J., Zhang, S., & Zhang, X. (2025). Classifier-guided neural blind deconvolution: A physics-informed denoising module for bearing fault diagnosis under noisy conditions. Mechanical Systems and Signal Processing, 222, 111750. https://doi.org/10.1016/j.ymssp.2024.111750
  17. Wang, Q., & Xu, F. (2023). A novel rolling bearing fault diagnosis method based on adaptive denoising convolutional neural network under noise background. Measurement, 218, 113209. https://doi.org/10.1016/j.measurement.2023.113209
  18. Kim, Y., & Kim, Y.-K. (2024). Physics-informed time-frequency fusion network with attention for noise-robust bearing fault diagnosis. IEEE Access, 12, 12517–12532. https://doi.org/10.1109/ACCESS.2024.3355268
  19. Wang, H., Liu, Z., Peng, D., & Cheng, Z. (2022). Attention-guided joint learning CNN with noise robustness for bearing fault diagnosis and vibration signal denoising. ISA Transactions, 128, 470–484. https://doi.org/10.1016/j.isatra.2021.11.028
  20. Sun, H., Cao, X., Wang, C., & Gao, S. (2022). An interpretable anti-noise network for rolling bearing fault diagnosis based on FSWT. Measurement, 190, 110698. https://doi.org/10.1016/j.measurement.2022.110698
  21. He, D., Zhang, Z., Jin, Z., Zhang, F., Yi, C., & Liao, S. (2025). RTSMFFDE-HKRR: A fault diagnosis method for train bearing in noise environment. Measurement, 239, 115417. https://doi.org/10.1016/j.measurement.2024.115417
  22. Zhang, Y., Ren, Z., Feng, K., Yu, K., Beer, M., & Liu, Z. (2023). Universal source-free domain adaptation method for cross-domain fault diagnosis of machines. Mechanical Systems and Signal Processing, 191, 110159. https://doi.org/10.1016/j.ymssp.2023.110159
  23. Jia, S., Li, Y., Wang, X., Sun, D., & Deng, Z. (2023). Deep causal factorization network: A novel domain generalization method for cross-machine bearing fault diagnosis. Mechanical Systems and Signal Processing, 192, 110228. https://doi.org/10.1016/j.ymssp.2023.110228
  24. Shu, Z., Peng, D., Wang, H., Mao, C., & Yu, Z. (2025). Time-frequency perception guided multi-level contrastive learning for rotating machinery fault diagnosis. Expert Systems with Applications, 293, 128664. https://doi.org/10.1016/j.eswa.2025.128664
  25. Tang, J., Xiao, J., Chen, W., Li, X., Wei, C., Ding, X., & Huang, W. (2024). A prior knowledge-enhanced self-supervised learning framework using time-frequency invariance for machinery intelligent fault diagnosis with small samples. Engineering Applications of Artificial Intelligence, 133, 108503. https://doi.org/10.1016/j.engappai.2024.108503
  26. Kim, Y., & Kim, Y.-K. (2023). Time-frequency multi-domain 1D convolutional neural network with channel-spatial attention for noise-robust bearing fault diagnosis. Sensors, 23(23), 9311. https://doi.org/10.3390/s23239311
  27. Zhang, W.-T., Liu, L., Cui, D., Ma, Y.-Y., & Huang, J. (2023). An anti-noise convolutional neural network for bearing fault diagnosis based on multi-channel data. Sensors, 23(15), 6654. https://doi.org/10.3390/s23156654
  28. Xu, Z., Jia, Z., Wei, Y., Zhang, S., Jin, Z., & Dong, W. (2024). A strong anti-noise and easily deployable bearing fault diagnosis model based on time-frequency dual-channel Transformer. Measurement, 236, 115054. https://doi.org/10.1016/j.measurement.2024.115054
  29. Zhao, C., Zio, E., & Shen, W. (2024). Domain generalization for cross-domain fault diagnosis: An application-oriented perspective and a benchmark study. Reliability Engineering & System Safety, 245, 109964. https://doi.org/10.1016/j.ress.2024.109964
  30. Wang, Y., Gao, J., Wang, W., Yang, X., & Du, J. (2024). Curriculum learning-based domain generalization for cross-domain fault diagnosis with category shift. Mechanical Systems and Signal Processing, 212, 111295. https://doi.org/10.1016/j.ymssp.2024.111295
  31. Huang, K., Ren, Z., Zhu, L., Lin, T., Zhu, Y., Zeng, L., & Wan, J. (2025). A three-stage bearing transfer fault diagnosis method for large domain shift scenarios. Reliability Engineering & System Safety, 254, 110641. https://doi.org/10.1016/j.ress.2024.110641
  32. Chen, Y., Yue, J., Liu, Z., & Chen, J. (2025). A semi-supervised wise-attention weighted prototype network for rolling bearing fault diagnosis under noisy and limited labeled data conditions. Neurocomputing, 647, 130563. https://doi.org/10.1016/j.neucom.2025.130563
  33. Liu, R., Ma, W., Kuang, F., Guo, J., & Zhao, N. (2024). A source free robust domain adaptation approach with pseudo-labels uncertainty estimation for rolling bearing fault diagnosis under limited sample conditions. Knowledge-Based Systems, 304, 112443. https://doi.org/10.1016/j.knosys.2024.112443
  34. Li, Y., Wang, T., Xie, J., Yang, J., Pan, T., Niu, B., & Yu, X. (2025). A temporal cross-contrastive self-supervised learning framework for high-speed train bearing fault diagnosis: Addressing limited labeling and speed variability. Engineering Applications of Artificial Intelligence, 158, 111537. https://doi.org/10.1016/j.engappai.2025.111537
  35. Zhang, Y., Yang, B., & Zhao, Z. (2025). Differential out-of-distribution error guidance for unknown bearing fault diagnosis with conditional diffusion model. Engineering Applications of Artificial Intelligence, 158, 111350. https://doi.org/10.1016/j.engappai.2025.111350
  36. Liao, R., Wang, C., Peng, F., Liang, W., Zhang, Y., & Zhang, X. (2024). DTM-Bearing: A novel framework for speed-invariant bearing fault diagnosis based on diffusion transformation model (DTM). IEEE Access, 12, 8875–8888. https://doi.org/10.1109/ACCESS.2024.3351935
  37. Zhang, Q., Xu, C., Li, J., Sun, Y., Bao, J., & Zhang, D. (2025). LLM-TSFD: An industrial time series human-in-the-loop fault diagnosis method based on a large language model. Expert Systems with Applications, 264, 125861. https://doi.org/10.1016/j.eswa.2024.125861
  38. Ni, Q., Ji, J. C., Halkon, B., Feng, K., & Nandi, A. K. (2023). Physics-informed residual network (PIResNet) for rolling element bearing fault diagnostics. Mechanical Systems and Signal Processing, 200, 110544. https://doi.org/10.1016/j.ymssp.2023.110544
  39. Zhang, H., Liu, Z., Si, H., Yu, K., Li, S., & Yan, Z. (2025). A lightweight fault diagnosis framework for hydro-turbine main shaft bearing under noise interference. IEEE Access, 13, 102133–102143. https://doi.org/10.1109/ACCESS.2025.3578682