Robust early detection of hunting on railway vehicles: single-sensor based methods and critical comparisons

K. Kritikakos, S.D. Fassois and J.S. Sakellariou

International Journal of Rail Transportation, 2024, vol. 12, issue 3, 437-457

Abstract: The problem of robust and early detection of railway hunting is considered and two single-sensor ‘dynamics-based’ methods employing adaptive Recursive AutoRegressive (RAR) modelling of the car-body lateral random vibration signal are postulated. The first is based on a Degree-of-Stochasticity (DS) measure of the vibration signal and the second on the minimum underlying Damping Ratio (DR). Fully automated tuning for both methods is achieved via Bayesian optimization. The performance of the methods is systematically assessed via high numbers of Monte Carlo simulations under various Scenarios employing a high-fidelity SIMPACK-based vehicle model and three performance criteria: the True Positive Rate (TPR), the False Positive Rate (FPR), and the Detection Delay Time (DDT) with respect to conventional hunting initiation. The results reveal very good performance and robustness to suspension faults and worn track conditions for both methods, with the DR-based exhibiting an edge. Its performance and achievable robustness are also shown to be clearly superior to those of three alternative methods.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/23248378.2023.2181237 (text/html)
Access to full text is restricted to subscribers.

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:taf:tjrtxx:v:12:y:2024:i:3:p:437-457

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjrt20

DOI: 10.1080/23248378.2023.2181237

Access Statistics for this article

International Journal of Rail Transportation is currently edited by Wanming Zhai and Kelvin C. P. Wang

More articles in International Journal of Rail Transportation from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().