Power Equipment Fault Diagnosis Model Based on Deep Transfer Learning with Balanced Distribution Adaptation

  • Kaijie WangEmail author
  • Bin Wu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11323)


In recent years, an increasing popularity of deep learning models has been widely used in the field of electricity. However, in previous studies, it is always assumed that the training data is sufficient, the training and the testing data are taken from the same feature distribution, which limits their performance on the imbalanced tasks. So, in order to tackle the imbalanced data distribution problem, this paper presents a new model of deep transfer network with balanced distribution adaptation, aiming to adaptively balance the importance of the marginal and conditional distribution discrepancies. By conducting comparative experiments, this model is proved to be effective and have achieved a better performance in both classification accuracy and domain adaptation effectiveness.


Transfer learning Domain adaptation Balanced distribution adaptation Deep transfer network Power data analysis 


  1. 1.
    Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)CrossRefGoogle Scholar
  2. 2.
    Weiss, K., Khoshgoftaar, T.M., Wang, D.: A survey of transfer learning. J. Big Data 3, 9 (2016)CrossRefGoogle Scholar
  3. 3.
    Long, M., Wang, J., Ding, G., Sun, J., Yu, P.S.: Transfer feature learning with joint distribution adaptation. In: IEEE International Conference on Computer Vision, pp. 2200–2207 (2013)Google Scholar
  4. 4.
    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)CrossRefGoogle Scholar
  5. 5.
    Li, J., Zhao, J., Lu, K.: Joint feature selection and structure preservation for domain adaptation. In: International Joint Conferences on Artificial Intelligence (2016)Google Scholar
  6. 6.
    Long, M., Wang, J., Ding, G., Sun, J., Yu, P.S.: Transfer joint matching for unsupervised domain adaptation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1410–1417 (2014)Google Scholar
  7. 7.
    Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks. Eprint Arxiv 27, 3320–3328 (2014)Google Scholar
  8. 8.
    Long, M., Zhu, H., Wang, J., Jordan, M.I.: Deep transfer learning with joint adaptation networks. In: International Conference on Machine Learning (2016)Google Scholar
  9. 9.
    Hsu, T.M.H., Chen, W.Y., Hou, C.-A., Tsai, Y.-H.H., Yeh, Y.-R., Wang, Y.-C.F.: Unsupervised domain adaptation with imbalanced cross-domain data. In: IEEE International Conference on Computer Vision, pp. 4121–4129 (2015)Google Scholar
  10. 10.
    Wang, J., Chen, Y., Hao, S., Feng, W., Shen, Z.: Balanced distribution adaptation for transfer learning. In: IEEE International Conference on Data Mining, pp. 1129–1134 (2017)Google Scholar
  11. 11.
    Costilla-Reyes, O., Scully, P., Ozanyan, K.B.: Deep neural networks for learning spatio-temporal features from tomography sensors. IEEE Trans. Industr. Electron. 65, 645–653 (2018)CrossRefGoogle Scholar
  12. 12.
    Khatami, A., Babaie, M., Tizhoosh, H.R., Khosravi, A., Nguyen, T., Nahavandi, S.: A sequential search-space shrinking using CNN transfer learning and a Radon projection pool for medical image retrieval. Expert Syst. Appl. 100, 224–233 (2018)CrossRefGoogle Scholar
  13. 13.
    Wei, Y., Zhang, Y., Yang, Q.: Learning to transfer. Eprint arxiv, arXiv:1708.05629 [cs.AI] (2017)
  14. 14.
    Qureshi, A.S., Khan, A., Zameer, A., Usman, A.: Wind power prediction using deep neural network based meta regression and transfer learning. Appl. Soft Comput. 58, 742–755 (2017)CrossRefGoogle Scholar
  15. 15.
    Lu, W., Liang, B., Cheng, Y., Meng, D., Yang, J., Zhang, T.: Deep model based domain adaptation for fault diagnosis. IEEE Trans. Industr. Electron. 64, 2296–2305 (2017)CrossRefGoogle Scholar
  16. 16.
    Zhang, J., Li, W., Ogunbona, P.: Joint geometrical and statistical alignment for visual domain adaptation. In: IEEE Conference on Computer Vision and Pattern Recognition (2017)Google Scholar
  17. 17.
    Li, S., Song, S., Huang, G.: Prediction reweighting for domain adaptation. IEEE Trans. Neural Netw. Learn. Syst. 99, 1–14 (2016)Google Scholar

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, School of Computer ScienceBeijing University of Posts and TelecommunicationsBeijingChina

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