Development of an optical sensing system for the detection of lubricity conditions on the rail gage face

Chen, Yang; Mirzaei, S. Morteza H.; Holton, Carvel; Ahmadian, Mehdi

doi:10.1080/23248378.2024.2309618

International Journal of Rail Transportation

2024

DOI: 10.1080/23248378.2024.2309618

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Development of an optical sensing system for the detection of lubricity conditions on the rail gage face

Yang Chen,

S. Morteza H. Mirzaei,

Carvel Holton

et al.

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2024

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Cited by 1 publication

References 26 publications

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In-Motion, Non-Contact Detection of Ties and Ballasts on Railroad Tracks

Mirzaei,

Radmehr,

Holton

et al. 2024

Applied Sciences

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This study aims to develop a robust and efficient system to identify ties and ballasts in motion using a variety of non-contact sensors mounted on a robotic rail cart. The sensors include distance LiDAR sensors and inductive proximity sensors for ferrous materials to collect data while traversing railroad tracks. Many existing tie/ballast health monitoring devices cannot be mounted on Hyrail vehicles for in-motion inspection due to their inability to filter out unwanted targets (i.e., ties or ballasts). The system studied here addresses that limitation by exploring several approaches based on distance LiDAR sensors. The first approach is based on calculating the running standard deviation of the measured distance from LiDAR sensors to tie or ballast surfaces. The second approach uses machine learning (ML) methods that combine two primary algorithms (Logistic Regression and Decision Tree) and three preprocessing methods (six models in total). The results indicate that the optimal configuration for non-contact, in-motion differentiation of ties and ballasts is integrating two distance LiDAR sensors with a Decision Tree model. This configuration provides rapid, accurate, and robust tie/ballast differentiation. The study also facilitates further sensor and inspection research and development in railroad track maintenance.

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In-Motion, Non-Contact Detection of Ties and Ballasts on Railroad Tracks

Mirzaei,

Radmehr,

Holton

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

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Development of an optical sensing system for the detection of lubricity conditions on the rail gage face

Cited by 1 publication

References 26 publications

In-Motion, Non-Contact Detection of Ties and Ballasts on Railroad Tracks

In-Motion, Non-Contact Detection of Ties and Ballasts on Railroad Tracks

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