| Title |
Railway track vibration analysis and complexity assessment based on H-ranks / |
| Authors |
Orinaitė, Ugnė ; Ragulskis, Minvydas |
| DOI |
10.15388/DAMSS.14.2023 |
| ISBN |
9786090709856 |
| Full Text |
|
| Is Part of |
DAMSS 2023:14th conference on data analysis methods for software systems, November 30 – December 2, 2023, Druskininkai, Lithuania.. Vilnius : Vilnius University press, 2023. p. 67.. ISBN 9786090709856 |
| Abstract [eng] |
This study addresses safety concerns in rail transport, with a specific focus on rail vehicle movement. Railway level crossings pose the highest accident risk due to the interaction of road and rail traffic. The most crucial safety aspect at unprotected railway crossings is the driver’s ability to see an approaching train. This study aims to mitigate these issues by developing a method to provide advance train warnings, regardless of visibility limitations. Signal analysis plays a pivotal role in various scientific and technical domains, such as transportation engineering. The H-rank method is introduced as a potential tool for signal analysis. The H-rank algorithm leverages advanced mathematical concepts, particularly matrix factorization and rank estimation, to extract valuable insights from signals. It enables the identification of underlying patterns, anomalies, and pertinent aspects within the signal. As an algebraic mathematical feature, the H-rank method can not only determine the order of linear recurrence but also assess the algebraic complexity of time series. This study examines three different types of experimental train track vibration signals generated by various train types; all signals have a common sampling rate. The study uses H-ranks as the method for vibration signal analysis, the reconstructed linear regression model takes place to indicate approaching trains. The method presented in this study relies on rail vibration measurements and can make independent predictions, not relying on other rail transport information systems. This study aims to showcase the extensive applicability of the H-rank approach in signal processing. It illustrates its capacity to reduce signal noise, enhance signal quality, and detect real-time signal variations. The outcomes of employing the H-rank algorithm underscore its advantages over conventional signal analysis techniques. |
| Published |
Vilnius : Vilnius University press, 2023 |
| Type |
Conference paper |
| Language |
English |
| Publication date |
2023 |
| CC license |
|
