Abstract
Transfer learning has been widely used in train bearing fault diagnosis. However, most existing methods are hindered under different working conditions in practical engineering applications. To fill this gap, this paper proposes an improved joint distribution adaptation algorithm (IJDA) based on Wasserstein distance. It first extracts multi-dimension features from time domain, frequency domain and entropy domain of raw signal to express the individual information of different fault types while reducing the number of input dimension. Meanwhile, by using Wasserstein distance as the metric of the K nearest neighbor algorithm, the distance between the source domain and target domain samples in the feature space is effectively pulled in, which improves the classification accuracy of JDA significantly. Experiments from two dataset with different probability distribution is designed, and results show that the effectiveness and robustness of the proposed method is superior to that of other state-of-art transfer learning methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Idriss, T., Jantunen, E.: A summary of fault modeling and predictive health monitoring of rolling element bearings. Mech. Syst. Signal Process. 60–61, 252–272 (2015)
Liu, Y.: Rolling bearing fault diagnosis based on low speed vibration signal of wheelset, pp. 1–2. Southwest Jiaotong University, Sichuan (2017). (in Chinese)
Zhao, X., Qin, Y., Fu, H., Jia, L., Zhang, X.: Blind source extraction based on EMD and temporal correlation for rolling element bearing fault diagnosis. Smart Resilient Transp. 3(1), 52–65 (2021)
Pan, S.J., Tsang, I.W., Kwok, J.T., Yang, Q.: Domain adaptation via transfer component analysis. IEEE Trans. Neural Netw. 22(2), 199–210 (2011)
Liu, J., et al.: Cross-view action recognition via view knowledge transfer. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2011), pp. 3209–3216. IEEE (2011)
Long, M., Wang, J., Ding, G., et al.: Transfer joint matching for unsupervised domain adaptation. In: IEEE Conference on Computer Vision & Pattern Recognition, pp. 1410–1417. IEEE (2014)
Long, M., Zhu, H., Wang, J., Jordan, M.I.: Deep transfer learning with joint adaptation networks. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 2208–2217 (2017). Proceedings of Machine Learning Research
Zhou, Y., Bu, W.: Multi-scale permutation entropy feature extraction and GK recognition for motor bearing fault. Modular Mach. Tool Autom. Manuf. Tech. (04), 70–74 (2021). (in Chinese)
Lei, Y., He, Z., Zi, Y., Qiao, H.: Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs. Mech. Syst. Signal Process. 21(5), 2280–2293 (2006)
Long, M., Wang, J., Ding, G., et al.: Transfer feature learning with joint distribution adaptation. In: Proceedings of the 2013 IEEE International Conference on Computer Vision, pp. 1–27. IEEE (2013)
Shen, J., Qu, Y., Zhang, W., et al.: Wasserstein distance guided representation learning for domain adaptation. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence, pp. 4058–4065. AAAI Press, Palo Alto (2018)
Acknowledgments
This research was partially funded by the National Natural Science Foundation of China under Grant 51905029, and by the Fundamental Research Funds for the Central Universities under Grant 2020JBM032, 2020JBZD011.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ding, Y., Xin, G., Li, Z., Zhong, Q., Jia, L. (2022). Fault Diagnosis of Train Wheelset Bearings Based on Improved Joint Distribution Adaptation. In: Qin, Y., Jia, L., Liang, J., Liu, Z., Diao, L., An, M. (eds) Proceedings of the 5th International Conference on Electrical Engineering and Information Technologies for Rail Transportation (EITRT) 2021. EITRT 2021. Lecture Notes in Electrical Engineering, vol 868. Springer, Singapore. https://doi.org/10.1007/978-981-16-9913-9_61
Download citation
DOI: https://doi.org/10.1007/978-981-16-9913-9_61
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-9912-2
Online ISBN: 978-981-16-9913-9
eBook Packages: EngineeringEngineering (R0)