Enhancing railway track maintenance with real-time ultrasonic and moisture sensing: Proactive and zone-specific management strategies

• The proposed system uses low-cost sensors improve railway track maintenance by reducing labor. • The system offers real-time data on ballast and moisture, stored for AI-based management. • It enhances safety by assessing ballast deficiencies and enabling timely maintenance. • The study recommends customized maintenance plans for different track zones and issues. The incorporation of ultrasonic and moisture sensors represents a significant advancement in railway track maintenance, offering a comprehensive and efficient means of assessing conditions, reducing time, and eliminating manual labor. Mounted on a trolley, these sensors simultaneously measure ballast cushion availability, moisture content, and humidity, providing real-time data stored in a cloud-based account for effective management. This data enables proactive maintenance, calculating ballast requirements using artificial intelligence during normal trolley runs. The system enhances safety and performance by assessing ballast availability and deficiencies along the track. Variations in defect lengths are evident between zones; for instance, the mud pumping zone ranges from 2 to 38 m with an average of 19 m and peak moisture content at 35 %. In contrast, zones with high freightage and track density exhibit more consistent defect lengths, averaging 13 and 10 m respectively, with moisture contents between 5 % and 15 %. The study emphasizes the necessity for tailored maintenance plans for each zone to ensure railway infrastructure effectiveness and safety.

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Publication Details

Journal
Results in Engineering
Published
2024-11-20
DOI
https://doi.org/10.1016/j.rineng.2024.103472
Citations
7
Primary Topic
Railway Engineering and Dynamics
Type
article
Field-Weighted Citation Impact
1.49

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article

Enhancing railway track maintenance with real-time ultrasonic and moisture sensing: Proactive and zone-specific management strategies

R Narayanamoorthi, Balasubramanian Murugesan, Tomáš Novák, Mukilan Poyyamozhi et al.
7 citations
Results in Engineering
Railway Engineering and Dynamics
1.49
article

Enhancing railway track maintenance with real-time ultrasonic and moisture sensing: Proactive and zone-specific management strategies

R Narayanamoorthi, Balasubramanian Murugesan, Tomáš Novák, Mukilan Poyyamozhi, Andal Saravana Kumar Devadharshini
article en
7 citations

Abstract

• The proposed system uses low-cost sensors improve railway track maintenance by reducing labor. • The system offers real-time data on ballast and moisture, stored for AI-based management. • It enhances safety by assessing ballast deficiencies and enabling timely maintenance. • The study recommends customized maintenance plans for different track zones and issues. The incorporation of ultrasonic and moisture sensors represents a significant advancement in railway track maintenance, offering a comprehensive and efficient means of assessing conditions, reducing time, and eliminating manual labor. Mounted on a trolley, these sensors simultaneously measure ballast cushion availability, moisture content, and humidity, providing real-time data stored in a cloud-based account for effective management. This data enables proactive maintenance, calculating ballast requirements using artificial intelligence during normal trolley runs. The system enhances safety and performance by assessing ballast availability and deficiencies along the track. Variations in defect lengths are evident between zones; for instance, the mud pumping zone ranges from 2 to 38 m with an average of 19 m and peak moisture content at 35 %. In contrast, zones with high freightage and track density exhibit more consistent defect lengths, averaging 13 and 10 m respectively, with moisture contents between 5 % and 15 %. The study emphasizes the necessity for tailored maintenance plans for each zone to ensure railway infrastructure effectiveness and safety.

Results in EngineeringVol. 24
VSB - Technical University of Ostrava (CZ), SRM Institute of Science and Technology (IN)
Virginia Soybean Board, Vysoká Škola Bánská - Technická Univerzita Ostrava
Openalex Percentile: Top 19%
Railway Engineering and Dynamics
1.49
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