Volume 23 Issue 3
Jun.  2023
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SHAO Hai-dong, LI Wei, LIN Jian, MIN Zhi-shan. Multi-mode data augmentation and fault diagnosis of gearbox using improved ACGAN[J]. Journal of Traffic and Transportation Engineering, 2023, 23(3): 188-197. doi: 10.19818/j.cnki.1671-1637.2023.03.014
Citation: SHAO Hai-dong, LI Wei, LIN Jian, MIN Zhi-shan. Multi-mode data augmentation and fault diagnosis of gearbox using improved ACGAN[J]. Journal of Traffic and Transportation Engineering, 2023, 23(3): 188-197. doi: 10.19818/j.cnki.1671-1637.2023.03.014
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Multi-mode data augmentation and fault diagnosis of gearbox using improved ACGAN

doi: 10.19818/j.cnki.1671-1637.2023.03.014

  • College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, Hunan, China

Funds:

National Key Research and Development Program of China 2020YFB1712100

National Natural Science Foundation of China 51905160

Natural Science Foundation of Hunan Province 2020JJ20017

More Information
  • Author Bio:

    SHAO Hai-dong(1990-), male, associate professor, PhD, hdshao@hnu.edu.cn

  • Received Date: 2023-01-08
    Available Online: 2023-07-07
  • Publish Date: 2023-06-25
    Abstract
  • To address the problems that the existing generative adversarial network (GAN) was difficult to efficiently generate multi-mode fault samples, and its training was unstable, an improved auxiliary classification GAN (ACGAN) was proposed for multi-mode data augmentation and intelligent fault diagnosis of the gearbox to ensure the safe operation of the vehicle. The independent classifier was introduced to construct a new ACGAN framework, and the compatibility between the classification accuracy and discriminant accuracy of classic ACGAN was improved. Wasserstein distances were used to define new adversarial loss functions with smooth properties, so as to overcome the disadvantages of the GAN, such as mode collapse and gradient vanishing. In order to improve the stability during the adversarial training process, the spectral normalization was used to replace the weight clipping to constrain the weight parameters of the discriminator. In order to verify the effectiveness and superiority of the improved ACGAN method, experimental analysis was performed on gearbox samples under six health conditions. Analysis results show that the fault samples generated by the improved ACGAN achieve better quality evaluation results at the data level and feature level, among which the evaluation index based on the structural similarity outperforms the comparison method by 0.249 3 on average, and the evaluation index based on the maximum mean difference outperforms the comparison method by 0.696 6 on average. The training process for the improved ACGAN is more stable. Its loss function has better convergence, and it has higher efficiency in multi-mode fault diagnosis scenarios with its training time reduced to 20% of the comparison method. In the case of missing fault samples, the generated fault samples of the improved ACGAN can effectively assist the training of intelligent fault diagnosis models based on deep learning, which can improve the diagnosis accuracy from 75.34% to 97.06%.

     

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    • References(31)
  • [1]
    LEI Ya-guo, XU Xue-fang, CAI Xiao, et al. Research on data quality assurance for health condition monitoring of machinery[J]. Journal of Mechanical Engineering, 2021, 57(4): 1-9. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB202104001.htm
    [2]
    ZHU Hai-yan, WANG Chao-wen, WU Ping-bo, et al. High-speed train gearbox housing vibration test based on small roller high-frequency excitation[J]. Journal of Traffic and Transportation Engineering, 2020, 20(5): 135-150. (in Chinese) doi: 10.19818/j.cnki.1671-1637.2020.05.011
    [3]
    JIANG Xing-xing, SONG Qiu-yu, ZHU Zhong-kui, et al. Gearbox fault diagnosis method based on convergent trend-guided variational mode decomposition[J]. Journal of Traffic and Transportation Engineering, 2022, 22(1): 177-189. (in Chinese) doi: 10.19818/j.cnki.1671-1637.2022.01.015
    [4]
    SHAO Hai-dong, LIN Jing, ZHANG Liang-wei, et al. A novel approach of multisensory fusion to collaborative fault diagnosis in maintenance[J]. Information Fusion, 2021, 74: 65-76. doi: 10.1016/j.inffus.2021.03.008
    [5]
    ZHAO Hui-min, LIU Jie, CHEN Hua-yue, et al. Intelligent diagnosis using continuous wavelet transform and gauss convolutional deep belief network[J]. IEEE Transactions on Reliability, 2020, 197: 105883.
    [6]
    SHAO Hai-dong, ZHANG Xiao-yang, CHENG Jun-sheng, et al. Intelligent fault diagnosis of bearing using enhanced deep transfer auto-encoder[J]. Journal of Mechanical Engineering, 2020, 56(9): 84-90. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB202009011.htm
    [7]
    ZHANG Long, ZHEN Can-zhuang, XIONG Guo-liang, et al. Locomotive bearing fault diagnosis based on deep time-frequency features[J]. Journal of Traffic and Transportation Engineering, 2021, 21(6): 247-258. (in Chinese) doi: 10.19818/j.cnki.1671-1637.2021.06.019
    [8]
    CHI Yong-wei, YANG Shi-xi, JIAO Wei-dong. A multi-label fault classification method for rolling bearing based on LSTM-RNN[J]. Journal of Vibration, Measurement and Diagnosis, 2020, 40(3): 563-571, 629. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCS202003021.htm
    [9]
    SURENDRAN R, KHALAF O, ROMERO C. Deep learning based intelligent industrial fault diagnosis model[J]. Computers, Materials and Continua, 2022, 70(3): 6323-6338. doi: 10.32604/cmc.2022.021716
    [10]
    CHEN Hong-tian, JIANG Bing, DING S, et al. Data-driven fault diagnosis for traction systems in high-speed trains: a survey, challenges, and perspectives[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(3): 1700-1716.
    [11]
    ZHAO Rui, YAN Ru-qiang, CHEN Zheng-hua, et al. Deep learning and its applications to machine health monitoring[J]. Mechanical Systems and Signal Processing, 2019, 115: 213-237.
    [12]
    SHEN Chang-qing, WANG Xu, WANG Dong, et al. Multi-scale convolution intra-class transfer learning for train bearing fault diagnosis[J]. Journal of Traffic and Transportation Engineering, 2020, 20(5): 151-164. (in Chinese) doi: 10.19818/j.cnki.1671-1637.2020.05.012
    [13]
    SHAO Hai-dong, XIAO Yi-ming, YAN Shen. Simulation data-driven enhanced unsupervised domain adaptation for bearing fault diagnosis[J]. Journal of Mechanical Engineering, 2023, 59(3): 76-85. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB202303008.htm
    [14]
    GOODFELLOW I J, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial networks[J]. Advances in Neural Information Processing Systems, 2014, 3: 2672-2680.
    [15]
    WANG Zi-rui, WANG Jun, WANG You-ren. An intelligent diagnosis scheme based on generative adversarial learning deep neural networks and its application to planetary gearbox fault pattern recognition[J]. Neurocomputing, 2018, 310(8): 213-222.
    [16]
    HE Qiang, TANG Xiang-hong, LI Chuan-jiang, et al. Bearing fault diagnosis method based on small sample data under unbalanced loads[J]. China Mechanical Engineering, 2021, 32(10): 1164-1171, 1180. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGJX202110004.htm
    [17]
    DAI Jun, WANG Jun, ZHU Zhong-kui, et al. Anomaly detection of mechanical systems based on generative adversarial network and auto-encoder[J]. Chinese Journal of Scientific Instrument, 2019, 40(9): 16-26. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YQXB201909002.htm
    [18]
    LIU Shen, CHEN Jing-long, QU Cheng, et al. LOSGAN: latent optimized stable GAN for intelligent fault diagnosis with limited data in rotating machinery[J]. Measurement Science and Technology, 2021, 32: 045101.
    [19]
    GONG Jia-liang, XU Xiao-dong, LEI Ying-ke. Unsupervised specific emitter identification method using radio-frequency fingerprint embedded InfoGAN[J]. IEEE Transactions on Information Forensics and Security, 2020, 15: 2898-2912.
    [20]
    RASTOGI R, GANGNANI R. Semi-supervised multi category classification with generative adversarial networks[C]//Springer. 2019 International Conference on Pattern Recognition and Machine Intelligence. Berlin: Springer, 2019: 286-294.
    [21]
    ODENA A, OLAH C, SHLENS J. Conditional image synthesis with auxiliary classifier GANs[C]//ACM. 2017 International Conference on Machine Learning. Sydney: ACM, 2017: 2642-2651.
    [22]
    SHAO Si-yu, WANG Pu, YAN Ru-qiang. Generative adversarial networks for data augmentation in machine fault diagnosis[J]. Computers in Industry, 2019, 106: 85-93.
    [23]
    GUO Qing-wen, LI Yi-bin, SONG Yan, et al. Intelligent fault diagnosis method based on full 1-D convolutional generative adversarial network[J]. IEEE Transactions on Industrial Informatics, 2020, 16(3): 2044-2053.
    [24]
    XIAO Xiong, XIAO Yu-xiong, ZHANG Yong-jun, et al. Research on the application of the data augmentation method based on 2D gray pixel images in the fault diagnosis of motor bearing[J]. Proceedings of the CSEE, 2021, 41(2): 738-749. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDC202102032.htm
    [25]
    SUN Can-fei, WANG You-ren, XIA Yu-bin. Fault diagnosis of helicopter planetary gear tooth crack based on SCAE-ACGAN[J]. Journal of Vibration, Measurement and Diagnosis, 2021, 41(3): 495-502, 620-621. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCS202103012.htm
    [26]
    LEE M H, SEOK J H, Controllable generative adversarial network[J]. IEEE Access, 2019, 7: 28158-28169.
    [27]
    ARJOVSKY M, CHINTALA S, BOTTOU L. Wasserstein generative adversarial networks[C]//ACM. International Conference on Machine Learning. Sydney: ACM, 2017: 214-233.
    [28]
    XIA Wei-hao, ZHANG Yu-lun, YANG Yu-jiu, et al. GAN inversion: a survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(3): 3121-3138.
    [29]
    ALATAT H, SIEGEL D, LEE J. A systematic methodology for gearbox health assessment and fault classification[J]. Journal of Prognostics and Health Management, 2011, 2(1): 16-22.
    [30]
    WEN Long, LI Xin-yu, GAO Liang, et al. A new convolutional neural network-based data-driven fault diagnosis method[J]. IEEE Transactions on Industrial Electronics, 2018, 65(7): 5990-5998.
    [31]
    MA Yan, YU Hai-jun, ZHONG Fa-sheng, et al. CT metal artifact reduction based on the residual encoder-decoder network[J]. Chinese Journal of Scientific Instrument, 2020, 41(8): 160-169. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YQXB202008018.htm
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