Education, Science, Technology, Innovation and Life
Open Access
Sign In
  1. Home
  2. Journals
  3. Journal of Electronics and Information Science
  4. Vol 10, Issue 2, 2025

A rolling bearing fault diagnosis method based on the improved sparrow search algorithm optimized VMD and multi-scale convolutional neural networks

Download as PDF

DOI: 10.23977/jeis.2025.100210 | Downloads: 0 | Views: 62

Author(s)

Gaolei Mao 1, Yali Sun 1

Affiliation(s)

1 School of Automation and Electrical Engineering, Lanzhou University of Technology, Lanzhou, China

Corresponding Author

Gaolei Mao

ABSTRACT

To address the issues of low diagnostic accuracy in traditional rolling bearing fault diagnosis models and the ineffective extraction of spatial and temporal features from vibration signals, this paper proposes a rolling bearing fault diagnosis method based on the improved sparrow search algorithm optimized VMD and multi-scale convolutional neural networks. First, the improved sparrow search algorithm is employed to adaptively optimize the penalty parameter and mode count in variational modal decomposition (VMD). This achieves finer frequency band segmentation and effectively suppresses energy leakage, thereby yielding high quality frequency domain representations. Second, a multi-scale convolutional neural networks (MSCNN) is constructed, with feature level fusion implemented. Subsequently, a bidirectional long short-term memory networks (BiLSTM) is introduced to model the temporal dependencies of the fused features, enabling fault mode learning. A softmax layer is employed to achieve multi-class classification. Finally experimental results and comparisons based on the CWRU bearing dataset demonstrate the effectiveness of the proposed method in the rolling bearing fault classification task, providing significant application value for achieving efficient and reliable bearing fault detection.

KEYWORDS

Fault diagnosis, Rolling bearings, Variational modal decomposition algorithm, Improved sparrow search algorithm, Multi-scale convolutional neural networks

CITE THIS PAPER

Gaolei Mao, Yali Sun, A rolling bearing fault diagnosis method based on the improved sparrow search algorithm optimized VMD and multi-scale convolutional neural networks. Journal of Electronics and Information Science (2025) Vol. 10: 82-89. DOI: http://dx.doi.org/10.23977/10.23977/jeis.2025.100210.

REFERENCES

[1] Lu K., Jin Y.L., Chen Y.S., et al. (2019) Review for Order Reduction Based on Proper Orthogonal Decomposition and Outlooks of Applications in Mechanical Systems [J]. Mechanical Systems and Signal Processing, 123(3): 264-297.
[2] Yan G., Chen J., Bai Y., et al. (2022) A Survey on Fault Diagnosis Approaches for Rolling Bearings of Railway Vehicles[J]. Processes, 10(4):724.
[3] Liu Z.P., Zhang L. (2020) A review of failure modes, condition monitoring and fault diagnosis methods for large-scale wind turbine bearings[J]. Measurement, 149: 107002.
[4] Yan R., Shang Z., Xu H., et al. (2023) Wavelet transform for rotary machine fault diagnosis: 10 years revisited[J]. Mechanical systems and signal processing, 200: 110545.
[5] Liu R., Yang B., Zio E., et al. (2018) Artificial intelligence for fault diagnosis of rotating machinery: A review[J]. Mechanical Systems and Signal Processing, 108: 33-47.
[6] Huang N.E., Shen Z., Long S.R., et al. (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings Mathematical Physical & Engineering Sciences, 454: 903-995.
[7] Dragomiretskiy K., Zosso D. (2014) Variational Mode Decomposition[J]. IEEE Transactions on Signal Processing, 62(3): 531-544.
[8] Chen G., Lu X., He L., et al. (2023) Subway train rolling bearing fault diagnosis method based on SSA-VMD. Equipment Manufacturing Technology, (7), 42–46.
[9] Wang Y., Cheng Y. (2021) Vibration signal analysis of cylinder head during engine combustion process[J]. Journal of Vibration and Shock, 40(13): 210-215, 254.
[10] Liu Y.S., Wei Z.G., Shu H.X., et al. (2023) Weak fault feature extraction of rolling bearings based on parameter adaptive VMD and MCKD[J]. Noise and Vibration Control, 43(3): 102-109.
[11] Hu P., Zhao C., Huang J., et al. (2023) Intelligent and small samples gear fault detection based on wavelet analysis and improved CNN[J]. Processes, 11(10):2969.
[12] Malhotra P., Ramakrishnan A., Anand G., et al. (2016) LSTM-based encoder-decoder for multi-sensor anomaly detection[J]. arxiv preprint arxiv:1607.00148.
[13] Chung J., Gulcehre C., Cho K.H., et al. (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling[J]. arxiv preprint arxiv:1412.3555.
[14] Xu Z.H., Pan T.L. (2021) Noise reduction method of fan gearbox vibration signal based on variational mode decomposition[J]. Journal of Mechanical & Electrical Engineering, 38(1): 129-132.
[15] Dong S.J., Li Y., Liang T., et al. (2022) Fault diagnosis method of rolling bearing based on CNN-BiLSTM under variable working conditions[J]. Journal of Vibration, Measurement & Diagnosis, 42(5): 1009-1016, 1040.
[16] Sun X.Y.,Xiang D.,Ding W.,et al. (2023) Research on individual pitch control of wind turbine based on sparrow search algorithm[J]. ACTA ENERGIAE SOLARIS SINICA,44(10):266-274.
[17] Lample G., Ballesteros M., Subramanian S., et al. (2016) Neural architectures for named entity recognition[J]. arxiv preprint arxiv:1603.01360 .
[18] L Smith W.A., Randall R.B. (2015) Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study[J]. Mechanical systems and signal processing, 64: 100-131.

Subscription
E-Mail Alert
Most popular papers
Call for Special Issue
Publication Ethics & OA Statement
Plagiarism Policy
Contact Us
Downloads: 13176
Visits: 542352

Sponsors, Associates, and Links

All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.