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A deep transfer learning method based on stacked autoencoder for cross-domain fault diagnosis

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  • Deng, Ziwei
  • Wang, Zhuoyue
  • Tang, Zhaohui
  • Huang, Keke
  • Zhu, Hongqiu

Abstract

In the actual industrial process, the distribution of historical training data and online testing data is always different due to the switching of operating modes and changes in climate conditions. At this time, the performance of traditional data-driven fault diagnosis methods based on the assumption that historical training data and online testing data follow the same distribution will degrade. Therefore, how to ensure the reliability of the fault diagnosis method for the distribution distortion is necessary yet challenge. In this paper, a cross-domain fault diagnosis method based on transferred stacked autoencoder is proposed. In detail, a stacked autoencoder is firstly used to extract features of a large amount of source domain data, and the features are classified to establish the source domain model. Then, a small amount of target domain data is introduced to fine-tune the source domain model to achieve domain adaptation. The effectiveness and superiority of the proposed deep transfer method was demonstrated through wind turbine system experiment and pump truck experiment. In addition, this paper also discusses the number of layers of the stacked autoencoder and model transfer strategies in detail to help practitioners understand the proposed method in practice.

Suggested Citation

  • Deng, Ziwei & Wang, Zhuoyue & Tang, Zhaohui & Huang, Keke & Zhu, Hongqiu, 2021. "A deep transfer learning method based on stacked autoencoder for cross-domain fault diagnosis," Applied Mathematics and Computation, Elsevier, vol. 408(C).
    • Handle: RePEc:eee:apmaco:v:408:y:2021:i:c:s0096300321004070
    DOI: 10.1016/j.amc.2021.126318
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    References listed on IDEAS

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    1. Chujie Tian & Jian Ma & Chunhong Zhang & Panpan Zhan, 2018. "A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network," Energies, MDPI, vol. 11(12), pages 1-13, December.
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    Cited by:

    1. Fuqiang Liu & Yandan Chen & Wenlong Deng & Mingliang Zhou, 2023. "Entropy-Optimized Fault Diagnosis Based on Unsupervised Domain Adaptation," Mathematics, MDPI, vol. 11(9), pages 1-18, April.
    2. Wang, Yun & Song, Mengmeng & Yang, Dazhi, 2024. "Local-global feature-based spatio-temporal wind speed forecasting with a sparse and dynamic graph," Energy, Elsevier, vol. 289(C).
    3. Gao, Fang & Xu, Zidong & Yin, Linfei, 2024. "Bayesian deep neural networks for spatio-temporal probabilistic optimal power flow with multi-source renewable energy," Applied Energy, Elsevier, vol. 353(PA).

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