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

Salvatore Cuomo, Mariapia De Rosa, Francesco Piccialli and Laura Pompameo

Mathematics and Computers in Simulation (MATCOM), 2024, vol. 223, issue C, 368-379

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.

Keywords: Numerical methods; Deep learning; Extended Kalman filter; Physics-informed neural network; Sensor fusion; Railway tracking; Vertical displacement (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378475424001526
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:223:y:2024:i:c:p:368-379

DOI: 10.1016/j.matcom.2024.04.026

Access Statistics for this article

Mathematics and Computers in Simulation (MATCOM) is currently edited by Robert Beauwens

More articles in Mathematics and Computers in Simulation (MATCOM) from Elsevier
Bibliographic data for series maintained by Catherine Liu ().