IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i17p6357-d1231346.html
   My bibliography  Save this article

Prediction of Residual Electrical Life in Railway Relays Based on Convolutional Neural Network Bidirectional Long Short-Term Memory

Author

Listed:
  • Shuxin Liu

    (School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

  • Yankai Li

    (School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

  • Shuyu Gao

    (School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

  • Chaojian Xing

    (School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

  • Jing Li

    (School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

  • Yundong Cao

    (School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

Abstract

In this paper, we address several issues with existing methods for predicting the residual electrical life of railway relays. These issues include the difficulty of single feature prediction in characterizing the degradation process, the neglect of temporal and backward–forward correlations in the degradation process, and low prediction accuracy. To overcome these challenges, we propose a novel approach that combines convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTM) to facilitate the life prediction of railway relays and provide an accurate data basis for the maintenance of railway relays. Firstly, we collected voltage and current signals from railway relay electrical life tests and extracted feature parameters that captured the relay’s operating state. Next, we applied Spearman correlation coefficient analysis combined with random forest importance analysis to perform double-feature selection. This process eliminates redundant feature parameters and identifies the optimal feature subset. Finally, we constructed a convolutional neural network bidirectional long short-term memory (CNN BiLSTM) prediction model to accurately predict the remaining electrical life of the railway relay. Through our analysis of the prediction results, we observed that the CNN BiLSTM model achieves an effective prediction accuracy of 96.3%. This accuracy is significantly higher, more stable, and more practical compared to other prediction models such as recurrent neural networks (RNNs), long short-term memory (LSTM), and BiLSTM models. Overall, our proposed CNN BiLSTM model offers higher accuracy, better stability, and greater practicality in predicting the remaining electrical life of railway relays.

Suggested Citation

  • Shuxin Liu & Yankai Li & Shuyu Gao & Chaojian Xing & Jing Li & Yundong Cao, 2023. "Prediction of Residual Electrical Life in Railway Relays Based on Convolutional Neural Network Bidirectional Long Short-Term Memory," Energies, MDPI, vol. 16(17), pages 1-21, September.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:17:p:6357-:d:1231346
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/17/6357/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/17/6357/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:17:p:6357-:d:1231346. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.