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A Long-Term Prediction Method of Computer Parameter Degradation Based on Curriculum Learning and Transfer Learning

Author

Listed:
  • Yuanhong Mao

    (Xi’an Microelectronics Technology Institute, Xi’an 710065, China)

  • Zhong Ma

    (Xi’an Microelectronics Technology Institute, Xi’an 710065, China)

  • Xi Liu

    (Xi’an Microelectronics Technology Institute, Xi’an 710065, China)

  • Pengchao He

    (Xi’an Microelectronics Technology Institute, Xi’an 710065, China)

  • Bo Chai

    (Xi’an Microelectronics Technology Institute, Xi’an 710065, China)

Abstract

The long-term prediction of the degradation of key computer parameters improves maintenance performance. Traditional prediction methods may suffer from cumulative errors in iterative prediction, which affect the model’s long-term prediction accuracy. Our network adopts curriculum learning and transfer learning methods, which can effectively solve this problem. The training network uses a dual-branch Siamese network. One branch intermixes the predicted and annotated data as input and uses curriculum learning to train. The other branch uses the original annotated data for training. To further align the hidden distributions of the two branches, the transfer learning method calculates the covariance matrices of the time series of the two branches by correlation alignment loss. A single branch is used in the test for prediction without increasing the inference computation. Compared with the current mainstream networks, our method can effectively improve the accuracy of long-term prediction with the improvements above.

Suggested Citation

  • Yuanhong Mao & Zhong Ma & Xi Liu & Pengchao He & Bo Chai, 2023. "A Long-Term Prediction Method of Computer Parameter Degradation Based on Curriculum Learning and Transfer Learning," Mathematics, MDPI, vol. 11(14), pages 1-15, July.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:14:p:3098-:d:1193463
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    References listed on IDEAS

    as
    1. Feiyue Deng & Yan Bi & Yongqiang Liu & Shaopu Yang, 2021. "Deep-Learning-Based Remaining Useful Life Prediction Based on a Multi-Scale Dilated Convolution Network," Mathematics, MDPI, vol. 9(23), pages 1-17, November.
    2. Sean J. Taylor & Benjamin Letham, 2018. "Forecasting at Scale," The American Statistician, Taylor & Francis Journals, vol. 72(1), pages 37-45, January.
    3. Lim, Bryan & Arık, Sercan Ö. & Loeff, Nicolas & Pfister, Tomas, 2021. "Temporal Fusion Transformers for interpretable multi-horizon time series forecasting," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1748-1764.
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