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Data-Driven Prognostics of the SOFC System Based on Dynamic Neural Network Models

Author

Listed:
  • Shan-Jen Cheng

    (Department of Mechanical Engineering, Lunghwa University of Science and Technology, Tao Yuan 333326, Taiwan)

  • Wen-Ken Li

    (Department of Mechanical Engineering, Chung Yuan Christian University, Tao Yuan 320314, Taiwan)

  • Te-Jen Chang

    (Department of Electrical and Electronic Engineering, Chung Cheng Institute of Technology, National Defense University, Tao Yuan 335009, Taiwan)

  • Chang-Hung Hsu

    (Department of Mechanical Engineering, Asia Eastern University of Science and Technology, New Taipei 220303, Taiwan)

Abstract

Prognostics technology is important for the sustainability of solid oxide fuel cell (SOFC) system commercialization, i.e., through failure prevention, reliability assessment, and the remaining useful life (RUL) estimation. To solve SOFC system issues, data-driven prognostics methods based on the dynamic neural network (DNN), one of non-linear models, were investigated in this study. Based on DNN model types, the neural network autoregressive (NNARX) model with external inputs, the neural network autoregressive moving average (NNARMAX) model with external inputs, and the neural network output error (NNOE) were utilized to predict the degradation trend and estimate the RUL. First, the degradation trend prediction was executed to evaluate the correctness of the proposed DNN model structures in the first learning phase. Then, the RUL was estimated on the basis of the degradation trend of the NN models in the second inference phase. The comparison test results show the prediction accuracy of the NNARX model is higher and the RUL estimation can be given within a smaller relative error than the NNARMAX and NNOE models. The evaluation criteria of the root mean square error and mean absolute error of the NNARX model are the smallest among these three models. Therefore, the proposed NNARX model can effectively and precisely provide degradation trend prediction and RUL estimation of the SOFC system.

Suggested Citation

  • Shan-Jen Cheng & Wen-Ken Li & Te-Jen Chang & Chang-Hung Hsu, 2021. "Data-Driven Prognostics of the SOFC System Based on Dynamic Neural Network Models," Energies, MDPI, vol. 14(18), pages 1-17, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:18:p:5841-:d:636143
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    References listed on IDEAS

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    1. Mumin Rao & Li Wang & Chuangting Chen & Kai Xiong & Mingfei Li & Zhengpeng Chen & Jiangbo Dong & Junli Xu & Xi Li, 2022. "Data-Driven State Prediction and Analysis of SOFC System Based on Deep Learning Method," Energies, MDPI, vol. 15(9), pages 1-15, April.

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