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A novel transfer learning fault diagnosis method based on Manifold Embedded Distribution Alignment with a little labeled data

Author

Listed:
  • Ke Zhao

    (Northwestern Polytechnical University)

  • Hongkai Jiang

    (Northwestern Polytechnical University)

  • Zhenghong Wu

    (Northwestern Polytechnical University)

  • Tengfei Lu

    (Northwestern Polytechnical University)

Abstract

Accurate identification of rolling bearing faults is quite significant for the stable operation of mechanical systems. However, for practical diagnosis issues, it is difficult to obtain abundant labeled data due to the change of operating conditions and complex working environment, which puts forward higher requirements on the ability of the diagnosis methods. To tackle the mentioned problem, a novel transfer learning method based on a little labeled data is proposed, which uses bidirectional gated recurrent unit (BiGRU) and Manifold Embedded Distribution Alignment (MEDA). Firstly, frequency spectrum datasets are utilized to remove the redundant information of raw vibration signals. Secondly, the BiGRU network is constructed to generate auxiliary samples that are utilized as source domain. Finally, MEDA, as the most powerful non-deep transfer learning method, is applied to align the distribution of these auxiliary samples generated by BiGRU and the unlabeled samples from target domain. Experiment results indicate the excellent performance of the proposed method under a little labeled data.

Suggested Citation

  • Ke Zhao & Hongkai Jiang & Zhenghong Wu & Tengfei Lu, 2022. "A novel transfer learning fault diagnosis method based on Manifold Embedded Distribution Alignment with a little labeled data," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 151-165, January.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:1:d:10.1007_s10845-020-01657-z
    DOI: 10.1007/s10845-020-01657-z
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    References listed on IDEAS

    as
    1. Chenxi Wu & Tefang Chen & Rong Jiang & Liwei Ning & Zheng Jiang, 2017. "A novel approach to wavelet selection and tree kernel construction for diagnosis of rolling element bearing fault," Journal of Intelligent Manufacturing, Springer, vol. 28(8), pages 1847-1858, December.
    2. Qiang Zhou & Ping Yan & Huayi Liu & Yang Xin, 2019. "A hybrid fault diagnosis method for mechanical components based on ontology and signal analysis," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1693-1715, April.
    3. Cong Wang & Meng Gan & Chang’an Zhu, 2017. "Intelligent fault diagnosis of rolling element bearings using sparse wavelet energy based on overcomplete DWT and basis pursuit," Journal of Intelligent Manufacturing, Springer, vol. 28(6), pages 1377-1391, August.
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    Cited by:

    1. Christian Neunzig & Dennis Möllensiep & Bernd Kuhlenkötter & Matthias Möller, 2024. "ML Pro: digital assistance system for interactive machine learning in production," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3479-3499, October.
    2. Ruohui Hu & Min Zhang & Zaiyu Xiang & Jiliang Mo, 2023. "Guided deep subdomain adaptation network for fault diagnosis of different types of rolling bearings," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2225-2240, June.

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