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Volume 8 Issue 7
Jul.  2021

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2021  >  8(7): 1345-1354
R. H. Jiao, K. X. Peng, and J. Dong, "Remaining Useful Life Prediction for a Roller in a Hot Strip Mill Based on Deep Recurrent Neural Networks," IEEE/CAA J. Autom. Sinica, vol. 8, no. 7, pp. 1345-1354, Jul. 2021. doi: 10.1109/JAS.2021.1004051
Citation: R. H. Jiao, K. X. Peng, and J. Dong, "Remaining Useful Life Prediction for a Roller in a Hot Strip Mill Based on Deep Recurrent Neural Networks," IEEE/CAA J. Autom. Sinica, vol. 8, no. 7, pp. 1345-1354, Jul. 2021. doi: 10.1109/JAS.2021.1004051
shu

Remaining Useful Life Prediction for a Roller in a Hot Strip Mill Based on Deep Recurrent Neural Networks

doi: 10.1109/JAS.2021.1004051
  • 1.

    Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China

  • 2.

    AVIC Xi’an Aviation Brake Technology Co., Ltd., Xi’an 710065, China

  • 3.

    Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing 100083, China

  • 4.

    Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China

Funds:  This work was supported by the Natural Science Foundation of China (NSFC) (61873024, 61773053), Fundamental Research Funds for the China Central Universities of USTB (FRF-TP-19-049A1Z), and the National Key RD Program of China (2017YFB0306403)
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    Abstract
  • Accurate estimation of the remaining useful life (RUL) and health state for rollers is of great significance to hot rolling production. It can provide decision support for roller management so as to improve the productivity of the hot rolling process. In addition, the RUL prediction for rollers is helpful in transitioning from the current regular maintenance strategy to conditional-based maintenance. Therefore, a new method that can extract coarse-grained and fine-grained features from batch data to predict the RUL of the rollers is proposed in this paper. Firstly, a new deep learning network architecture based on recurrent neural networks that can make full use of the extracted coarsegrained fine-grained features to estimate the heath indicator (HI) is developed, where the HI is able to indicate the health state of the roller. Following that, a state-space model is constructed to describe the HI, and the probabilistic distribution of RUL can be estimated by extrapolating the HI degradation model to a predefined failure threshold. Finally, application to a hot strip mill is given to verify the effectiveness of the proposed methods using data collected from an industrial site, and the relatively low RMSE and MAE values demonstrate its advantages compared with some other popular deep learning methods.

     

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    Highlights

    • A novel framework based on deep RNN is developed to estimate the RUL of rollers.
    • The proposed deep RNN extracts coarse-grained and fine-grained features to develop a HI.
    • The HI can automatically obtain the FT in the fault state without manual specification.
    • The RUL of the roller is determined by a comprehensive HI.

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