Abstract
Intelligent fault diagnosis has been widely used in the industry and plays a crucial role in the health management of machinery. In recent years, unsupervised domain adaptation (UDA) has been applied to fault diagnosis, showing excellent performance under variable working conditions. However, most existing UDA-based methods do not consider the temporal relations in the fault signal, resulting in sub-optimal performance. In this paper, we proposed a domain adversarial interaction network (DAIN) to solve this problem. By downsampling sub-sequences of fault signals and interacting with their features, DAIN can obtain feature representations containing the temporal relations. In addition, domain adversarial learning and maximum mean discrepancy (MMD) are applied to DAIN to align the domain discrepancy and distribution discrepancy of source and target domains. We conducted extensive experiments on the public Paderborn University (PU) dataset, and the results demonstrate that the proposed method can achieve higher cross-domain fault diagnosis accuracy than the existing 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
References
Borgwardt, K.M., Gretton, A., Rasch, M.J., Kriegel, H.P., Schölkopf, B., Smola, A.J.: Integrating structured biological data by kernel maximum mean discrepancy. Bioinformatics 22(14), e49–e57 (2006)
Chen, Z., Gryllias, K., Li, W.: Mechanical fault diagnosis using convolutional neural networks and extreme learning machine. Mech. Syst. Signal Process. 133, 106272 (2019)
Diederik, K., Jimmy, B., et al.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 pp. 273–297 (2014)
Fan, H., Zhang, F., Gao, Y.: Self-supervised time series representation learning by inter-intra relational reasoning. arXiv preprint arXiv:2011.13548 (2020)
Fan, H., Zhang, F., Wang, R., Xi, L., Li, Z.: Correlation-aware deep generative model for unsupervised anomaly detection. In: Lauw, H.W., Wong, R.C.-W., Ntoulas, A., Lim, E.-P., Ng, S.-K., Pan, S.J. (eds.) PAKDD 2020. LNCS (LNAI), vol. 12085, pp. 688–700. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-47436-2_52
Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res 17(1), 2030–2096 (2016)
Gu, Y.-K., Zhou, X.-Q., Yu, D.-P., Shen, Y.-J.: Fault diagnosis method of rolling bearing using principal component analysis and support vector machine. J. Mech. Sci. Technol. 32(11), 5079–5088 (2018). https://doi.org/10.1007/s12206-018-1004-0
Lessmeier, C., Kimotho, J.K., Zimmer, D., Sextro, W.: Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: a benchmark data set for data-driven classification. In: PHM Society European Conference, vol. 3 (2016)
Li, T., Zhao, Z., Sun, C., Yan, R., Chen, X.: Domain adversarial graph convolutional network for fault diagnosis under variable working conditions. IEEE Trans. Instrum. Meas. 70, 1–10 (2021)
Liu, M., Zeng, A., Xu, Z., Lai, Q., Xu, Q.: Time series is a special sequence: forecasting with sample convolution and interaction. arXiv preprint arXiv:2106.09305 (2021)
Liu, R., Yang, B., Zio, E., Chen, X.: Artificial intelligence for fault diagnosis of rotating machinery: a review. Mech. Syst. Signal Process. 108, 33–47 (2018)
Liu, Z.H., Jiang, L.B., Wei, H.L., Chen, L., Li, X.H.: Optimal transport-based deep domain adaptation approach for fault diagnosis of rotating machine. IEEE Trans. Instrum. Meas. 70, 1–12 (2021)
Long, M., Cao, Y., Wang, J., Jordan, M.: Learning transferable features with deep adaptation networks. In: International Conference on Machine Learning, pp. 97–105. PMLR (2015)
Long, M., Cao, Z., Wang, J., Jordan, M.I.: Conditional adversarial domain adaptation. In: 31st Proceedings of the conference on Advances in Neural Information Processing Systems (2018)
Long, M., Zhu, H., Wang, J., Jordan, M.I.: Deep transfer learning with joint adaptation networks. In: International Conference on Machine Learning, pp. 2208–2217. PMLR (2017)
Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(11) (2008)
Pandya, D., Upadhyay, S.H., Harsha, S.P.: Fault diagnosis of rolling element bearing by using multinomial logistic regression and wavelet packet transform. Soft. Comput. 18(2), 255–266 (2014)
Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: 32nd Proceedings Conference on Advances in Neural Information Processing Systems (2019)
Sun, B., Saenko, K.: Deep CORAL: correlation alignment for deep domain adaptation. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9915, pp. 443–450. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49409-8_35
Wang, X., Liu, F.: Triplet loss guided adversarial domain adaptation for bearing fault diagnosis. Sensors 20(1), 320 (2020)
Yin, A., Yan, Y., Zhang, Z., Li, C., Sánchez, R.V.: Fault diagnosis of wind turbine gearbox based on the optimized lSTM neural network with cosine loss. Sensors 20(8), 2339 (2020)
Zhao, Z., et al.: Applications of unsupervised deep transfer learning to intelligent fault diagnosis: a survey and comparative study. IEEE Trans. Instrum. Measur. 70 (2021)
Acknowledgement
This work is partially supported by the Open Fund Project of Computer Science and Technology Application-Oriented Discipline of Minjiang University under Grant MJUKF-JK202004.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Lu, W. et al. (2023). Domain Adversarial Interaction Network for Cross-Domain Fault Diagnosis. In: Xu, Y., Yan, H., Teng, H., Cai, J., Li, J. (eds) Machine Learning for Cyber Security. ML4CS 2022. Lecture Notes in Computer Science, vol 13656. Springer, Cham. https://doi.org/10.1007/978-3-031-20099-1_37
Download citation
DOI: https://doi.org/10.1007/978-3-031-20099-1_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-20098-4
Online ISBN: 978-3-031-20099-1
eBook Packages: Computer ScienceComputer Science (R0)