Article Details

Abstract

In this paper, Improved Multi-Scale Convolutional Networks bearing fault diagnosis method is proposed to solve the problems of Convolutional Neural Network in complex environments, such as easy to be disturbed, difficult to extract rich fault features from fixed receptive field and low diagnosis accuracy. Firstly, the original vibration signal is preprocessed. Secondly, the convolution kernels of different receptive fields are used to extract multi-scale features to effectively capture diversified fault information. Thirdly, the Self-Attention Mechanism is introduced to enable the model to dynamically calculate and adjust the weight of each position in the feature map, and adaptively enhance the key fault features. Finally, the fully connected layer is used to classify the extracted features to achieve accurate diagnosis. The experimental results show that the diagnosis accuracy of the method on the public dataset reaches about 98%, and it shows good anti-noise and generalization ability under different signal-to-noise ratio conditions.

Keywords

Multi-Scale Convolutional Networks; feature extraction; Self-Attention Mechanism; bearing fault diagnosis

References

1. [1] TANG H T，GAO S B，WANG L，et al. A Novel Intelligent Fault Diagnosis Method for Rolling Bearings Based on Wasserstein Generative Adversarial Network and Convolutional Neural Network under Unbalanced Dataset [J/OL].Sensors，2021，21（20）：6754.[2024-09-28].https://doi.org/10.3390/s21206754.
2. [2] ZHANG S，ZHANG S B，WANG B N，et al. Deep Learning Algorithms for Bearing Fault Diagnostics—A comprehensive Review [J].IEEE Access，2020，8：29857-29881.
3. [3] RIBEIRO JUNIOR R F，DOS SANTOS AREIAS I A，CAMPOS M M，et al. Fault Detection and Diagnosis in Electric Motors Using Convolution Neural Network and Short-Time Fourier Transform [J].Journal of Vibration Engineering & Technologies，2022，10（7）：2531-2542.
4. [4] Hu Fuqing, Chen Huipeng, Cheng Zhe, etc Fault diagnosis method for planetary gearbox based on empirical mode decomposition and deep convolutional neural network [J]. Journal of Mechanical Engineering, 2019, 55 (7): 9-18
5. [5] TARY J B，HERRERA R H，VAN DER BAAN M. Analysis of Time-Varying Signals Using Continuous Wavelet and Synchrosqueezed Transforms [J].Philosophical Transactions of the Royal Society A：Mathematical，Physical and Engineering Sciences，2018，376（2126）：20170254-20170254.
6. [6] YU J B. Local and Nonlocal Preserving Projection for Bearing Defect Classification and Performance Assessment [J].IEEE Transactions on Industrial Electronics，2012，59（5）：2363-2376.
7. [7] ZHANG X Y，LI C S，WANG X B，et al. A Novel Fault Diagnosis Procedure Based on Improved Symplectic Geometry Mode Decomposition and Optimized SVM [J/OL].Measurement，2021，173：108644.[2024-010-18].https://www.sciencedirect.com/science/article/abs/pii/S026322412031160X?via%3Dihub.
8. [8] YU W K，ZHAO C H，HUANG B. MoniNet With Concurrent Analytics of Temporal and Spatial Information for Fault Detection in Industrial Processes [J].IEEE Transactions on Cybernetics，2021，52（8）：8340-8351.
9. [9] JANSSENS O，SLAVKOVIKJ V，VERVISCH B，et al. Convolutional Neural Network Based Fault Detection for Rotating Machinery [J].Journal of Sound and Vibration，2016，377：331-345.
10. [10] MA Y F，JIA X S，BAI H J，et al. A New Fault Diagnosis Method Based on Convolutional Neural Network and Compressive Sensing [J].Journal of Mechanical Science and Technology，2019，33（11）：5177-5188.
11. [11] Sun Qichun, Li Yuanyuan Research on Optimizing CNN for Rolling Bearing Fault Diagnosis Using DE Algorithm [J]. Noise and Vibration Control, 2022, 42 (4): 165-171+176
12. [12] ZHAI X D，QIAO F，MA Y M，et al. A Novel Fault Diagnosis Method Under Dynamic Working Conditions Based on A CNN With an Adaptive Learning rate [J].IEEE Transactions on Instrumentation and Measurement，2022，71：1-12.
13. [13] PENG D D，LIU Z L，WANG H，et al. A Novel Deeper One-Dimensional CNN With Residual Learning for Fault Diagnosis of Wheelset Bearings in High-Speed Trains [J].IEEE Access，2018，7：10278-10293.
14. [14] JI M，CHU C S，YANG J H. A Joint Deep Learning Model for Bearing Fault Diagnosis in Noisy Environments [J].Journal of Mechanical Science and Technology，2024，38（7）：3265-3281.
15. [15] HUANG W Y，CHENG J S，YANG Y，et al. An Improved Deep Convolutional Neural Network With Multi-scale Information for Bearing Fault Diagnosis [J].Neurocomputing，2019，359：77-92.
16. [16] Wan Xin, Niu Yuguang Variable working condition bearing fault diagnosis based on improved convolutional neural network [J]. Bearing, 2024 (8): 68-73+79
17. [17] WANG H，LIU Z，PENG D，et al. Attention-Guided joint Learning CNN with Noise Robustness for Bearing Fault Diagnosis and Vibration Signal Denoising [J].ISA transactions，2022，128：470-484.
18. [18] Wang Zumin, Zhang Zhihao, Qin Jing, etc Overview of Mechanical Fault Diagnosis Technology Based on Convolutional Neural Networks [J]. Computer Applications, 2022, 42 (4): 1036-1043
19. [19] HUANG K，ZHU L B，REN Z J，et al. An Improved Fault Diagnosis Method for Rolling Bearings Based on 1D_CNN Considering Noise and Working Condition Interference [J/OL].Machines，2024，12（6）：383.[2024-10-18].https://doi.org/10.3390/machines12060383.
20. [20] LI J，BAO Y，LIU W X，et al. Twins transformer: Cross-Attention Based Two-Branch Transformer Network for Rotating Bearing Fault Diagnosis [J].Measurement，2023，223：113687.
21. [21] Zhang Zhiyi Research on Rolling Bearing Fault Diagnosis Technology Based on Multi scale Convolutional Neural Network [D]. Taiyuan: North China University, 2024
22. [22] LIANG H P，ZHAO X Q. Rolling Bearing Fault Diagnosis Based on one-Dimensional Dilated Convolution Network with Residual Connection [J].IEEE Access，2021，9：31078-31091.
23. [23] Zhang Wei Research on Bearing Fault Diagnosis Algorithm Based on Convolutional Neural Network [D]. Harbin: Harbin Institute of Technology, 2017

Funding

Provincial Key Project of Natural Science Research in Anhui Universities (KJ2020A0300)

About the authors

Gong Yingying (1999-), female, Han ethnicity, from Zhenjiang, Jiangsu, is currently pursuing a master's degree in intelligent fault diagnosis research.