IDEAS home Printed from https://ideas.repec.org/a/eee/matcom/v223y2024icp368-379.html
   My bibliography  Save this article

Railway safety through predictive vertical displacement analysis using the PINN-EKF synergy

Author

Listed:
  • Cuomo, Salvatore
  • De Rosa, Mariapia
  • Piccialli, Francesco
  • Pompameo, Laura

Abstract

Scientific Machine Learning (SciML) finds extensive application in daily life, industry, and scientific research. Specifically, in railway data analysis, it utilizes tools such as time series analysis, classification, and data visualization. Among these, monitoring vertical displacement, or the movement of railway components relative to a fixed point, is vital. This indicates changes in the elevation of railway infrastructure, highlighting track settlement and structural shifts. However, relying solely on single sensor measurements for these displacement calculations can introduce inaccuracies. To overcome this, sensor fusion techniques are employed. These techniques employ advanced algorithms to combine data from multiple sensors, thereby enhancing accuracy. They consider the individual characteristics of each sensor, effectively mitigating the limitations of any single sensor. This study introduces a hybrid approach that combines the Extended Kalman Filter (EKF) with the Physics-Informed Neural Network (PINN) to refine predictive data analytics in dynamic railway environments. The experimental results underscore the efficacy of this innovative methodology.

Suggested Citation

  • Cuomo, Salvatore & De Rosa, Mariapia & Piccialli, Francesco & Pompameo, Laura, 2024. "Railway safety through predictive vertical displacement analysis using the PINN-EKF synergy," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 223(C), pages 368-379.
  • Handle: RePEc:eee:matcom:v:223:y:2024:i:c:p:368-379
    DOI: 10.1016/j.matcom.2024.04.026
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378475424001526
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.matcom.2024.04.026?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:eee:matcom:v:223:y:2024:i:c:p:368-379. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/mathematics-and-computers-in-simulation/ .

    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.