Bearing fault diagnosis based on the combined use of RepVGG and CapsNet

Aijun1, L Mingyang1, YANG Minying2, CHEN Xiaomin1

Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (14) : 301-307.

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Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (14) : 301-307.

Bearing fault diagnosis based on the combined use of RepVGG and CapsNet

  • Aijun1, L Mingyang1, YANG Minying2, CHEN Xiaomin1
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Abstract

How to improve feature extraction ability and extract spatial information of features is the key to achieving high-precision fault diagnosis. RepVGG and other deep convolutional neural networks ignore the spatial information of features, while CapsNet has limited feature extraction ability due to its shallow network level. In order to solve the above problems, a bearing fault diagnosis method based on the combination of RepVGG and CapsNet was proposed. First, the vibration signals of different measuring points were obtained, converted into two-dimensional feature maps by GADF, and concatenated along the channel direction. Then, the RepVGG network was selected as the pre-convolutional layer to realize feature extraction and fusion of multi-dimensional vibration signals. Finally, CapsNet extracted the features’ spatial information to realize bearing fault diagnosis. The experimental results show that the fault diagnosis method based on the combination of RepVGG and CapsNet has excellent fault recognition performance and noise resistance.

Key words

bearing / fault diagnosis / deep learning / convolutional neural network

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Aijun1, L Mingyang1, YANG Minying2, CHEN Xiaomin1. Bearing fault diagnosis based on the combined use of RepVGG and CapsNet[J]. Journal of Vibration and Shock, 2024, 43(14): 301-307

References

[1] 马增强,李亚超,刘政,等.基于变分模态分解和Teager能量算子的滚动轴承故障特征提取[J].振动与冲击,2016,35(13):134-139. MA Zeng-qiang, LI Ya-chao, LIU Zheng, et al. Rolling bearing fault feature extraction based on variational mode decomposition and Teager energy operator[J]. Journal of Vibration and Shock, 2016, 35(13): 134-139. [2] WANG Z G, OATES T. Imaging time-series to improve classification and imputation[C]// Proceedings of the Twenty-fourth International Joint Conference on Artificial Intelligence. Buenos Aires: IJCAI, 2015. 3939-3945. [3] 侯东晓,穆金涛,方成,等.基于GADF与引入迁移学习的ResNet34对变速轴承的故障诊断[J].东北大学学报(自然科学版),2022,43(03):383-389. HOU Dong-xiao, MU Jin-tao, FANG Cheng, et al. Fault diagnosis of variable speed bearings based on GADF and ResNet34 introduced transfer learning[J]. Journal of Northeastern University (Natural Science), 2022, 43(3): 383-389. [4] 刘红军,魏旭阳.基于GADF与卷积神经网络的滚动轴承故障诊断研究[J].机电工程,2021,38(05):587-591,622. LIU Hong-jun, WEI Xu-yang. Rolling bearing fault diagnosis based on GADF and convolutional neural network[J]. Journal of Mechanical & Electrical Engineering, 2021, 38(5): 587-591, 622. [5] SZEGEDY C, LIU W, JIA Y Q, et al. Going deeper with convolutions[C]// 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston: IEEE, 2015. 1-9. [6] IOFFE S, SZEGEDY C. Batch normalization: accelerating deep network training by reducing internal covariate shift[C]// International Conference on Machine Learning. Lille: ACM, 2015. 448-456. [7] SZEGEDY C, VANHOUCKE V, IOFFE S, et al. Rethinking the Inception architecture for computer vision[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2016. 2818-2826. [8] SZEGEDY C, IOFFE S, VANHOUCKE V, et al. Inception-v4, Inception-ResNet and the impact of residual connections on learning[C]// Thirty-First AAAI Conference on Artificial Intelligence. San Francisco: AAAI, 2017. 4278-4284. [9] He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2016. 770-778. [10] DING X, ZHANG X, MA N, et al. RepVGG: Making VGG-style ConvNets great again[C]// 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 2021. 13728-13737. [11] 周涛,罗响,朱莉.基于一维RepVGG协同领域自适应的电机滚动轴承故障诊断[J].微特电机,2023,51(04):1-7. ZHOU Tao, LUO Xiang, ZHU Li. Motor rolling bearings fault diagnosis based on one-dimensional RepVGG and domain adaptation[J]. Small & Special Electrical Machines, 2023, 51(4): 1-7. [12] DING S S, CHEN R W, LIU H, et al. An improved re-parameterized visual geometry group network for rolling bearing fault diagnosis[J]. Review of Scientific Instruments, 2023, 94(3): 035007. [13] SABOUR S, FROSST N, HINTON G E. Dynamic routing between capsules[C]// Advances in Neural Information Processing Systems 30. Long Beach: MIT Press, 2017. [14] 孙岩,彭高亮.改进胶囊网络的滚动轴承故障诊断方法[J].哈尔滨工业大学学报,2021,53(1):23-28. SUN Yan, PENG Gaoliang. Improved capsule network method for rolling bearing fault diagnosis[J]. Journal of Harbin Institute of Technology, 2021, 53(1): 23-28. [15] 杨平,苏燕辰,张振.基于卷积胶囊网络的滚动轴承故障诊断研究[J].振动与冲击,2020,39(4):55-62. YANG Ping, SU Yan-chen, ZHANG Zhen. A study on rolling bearing fault diagnosis based on convolution capsule network[J]. Journal of Vibration and Shock, 2020, 39(4): 55-62. [16] LI X Y, KONG X W, ZHANG J Q, et al. A study on fault diagnosis of bearing pitting under different speed condition based on an improved inception capsule network[J]. Measurement, 2021, 181: 109656. [17] HAN T, MA R Y, ZHENG J G, Combination bidirectional long short-term memory and capsule network for rotating machinery fault diagnosis[J], Measurement, 2021, 176: 109208. [18] 杨洁,万安平,王景霖,等.基于多传感器融合卷积神经网络的航空发动机轴承故障诊断[J].中国电机工程学报,2022,42(13):4933-4942. YANG Jie, WAN An-ping, WANG Jing-lin, et al. Aeroengine bearing fault diagnosis based on convolutional neural network for multi-sensor information fusion[J]. Proceedings of the CSEE, 2022, 42(13): 4933-4942. [19] WANG H Q, LI S, SONG L Y, et al. A novel convolutional neural network based fault recognition method via image fusion of multi-vibration-signals[J]. Computers in Industry, 2019, 105: 182-190. [20] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[C]// 3rd International Conference on Learning Representations. San Diego: ICLR, 2015. [21] TAN M X, LE Q V. EfficientNet: Rethinking model scaling for convolutional neural networks[C]// International Conference on Machine Learning. Long Beach: ACM, 2019. 6105-6114.
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