Recognition of state variation of ballast bed using integrated multi-dimensional motion characteristics of ballast particles

Haonan Xi, Longlong Fu, Shuchen Wang, Yongjia Qiu, Shunhua Zhou and Binglong Wang

International Journal of Rail Transportation, 2025, vol. 13, issue 6, 1178-1199

Abstract: Wireless particle sensors show potential in direct recognition of the service state of ballast bed. Existing research has found that particles’ motion in a single dimension shows spatiotemporal randomness, which brings disadvantages to recognize the service state of ballast bed using particle motion characteristics. This study proposes an approach to integrate particle’s multi-dimensional motion characteristics to perform high-efficiency recognition of the state variation of ballast bed. Here, the state variation of ballast bed refers to the change of moisture content and local void volume. First, time-series models are established based on particle motion data obtained from initial states, and the damage-sensitive features and damage thresholds are extracted. Subsequently, the outlier proportions under various deteriorated states are obtained using Mahalanobis distance-based detection. Finally, the dimensionless parameter, outlier proportion, is utilized to integrate particle’s multi-dimensional motion characteristics. The results indicate that the integrated total outlier proportion is able to always reflect the characteristics of particle’s dominant motion, even the mode and direction of the dominant motion vary by particle sensors at different location. This indicates that the integrated total outlier proportion has potential to serve as a general indicator to evaluate the service state of ballast bed.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/23248378.2025.2465258 (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:13:y:2025:i:6:p:1178-1199

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

DOI: 10.1080/23248378.2025.2465258

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 ().