Fault Diagnosis Method of Rolling Bearing Combining Time-frequency Analysis with Deep Learning

REN Shengjie; GUO Weichao; SHU Dingzhen; TANG Aofei; GAO Xinqin; LI Yan

doi:10.13433/j.cnki.1003-8728.20200575

Volume 42 Issue 1

Jan. 2023

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Article Navigation > Mechanical Science and Technology for Aerospace Engineering > 2023 > 42(1): 149-158

REN Shengjie, GUO Weichao, SHU Dingzhen, TANG Aofei, GAO Xinqin, LI Yan. Fault Diagnosis Method of Rolling Bearing Combining Time-frequency Analysis with Deep Learning[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(1): 149-158. doi: 10.13433/j.cnki.1003-8728.20200575

Citation:

REN Shengjie, GUO Weichao, SHU Dingzhen, TANG Aofei, GAO Xinqin, LI Yan. Fault Diagnosis Method of Rolling Bearing Combining Time-frequency Analysis with Deep Learning[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(1): 149-158. doi: 10.13433/j.cnki.1003-8728.20200575

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Fault Diagnosis Method of Rolling Bearing Combining Time-frequency Analysis with Deep Learning

doi: 10.13433/j.cnki.1003-8728.20200575

School of Mechanical and Precision Instrument Engineering, Xi′an University of Technology, Xi′an 710048, China

Received Date: 2021-02-02
Publish Date: 2023-01-25

Abstract

Abstract

Rolling bearings are widely used in rotating machinery, and the working conditions of the bearings seriously affect the normal operation of mechanical equipment. In order to improve the accuracy of bearing fault diagnosis, a new fault diagnosis method of rolling bearing combining time-frequency analysis with deep learning is proposed in this paper. Firstly, ten different time-frequency analysis methods are analyzed and compared. Then, the fault diagnosis model for rolling bearings using deep learning is established, and the transfer learning is applied to overcome the problem led by small number of training samples. By contrast, the accuracy of constant Q transform (CQT) can reach 100%. Finally, the effectiveness and reliability of the proposed method are verified via the experimental data. The recognition accuracies under different working loads and noise environment are evaluated respectively, and are compared to the results obtained by other methods in references. The results show that the proposed method has better robustness and higher recognition accuracy under different working environment conditions.
- rolling bearing,
- fault diagnosis,
- time-frequency analysis,
- deep learning,
- transfer learning

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References(24)

References

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