Enhancing Railway Safety with an IoT Based System for Real Time Fault Detection and Crack Monitoring

Safety of railway is the major problem worldwide. It has problems like cracks or any fault in the railway tracks. These problems can cause severe accidents if it is not detected regularly and early. In traditional fault finding and crack detecting method, the manual method is involved which is very slow and consist of many errors. By considering all these challenges and issues in track detection method, we have implemented an advanced IoT based railway crack and fault-finding system. It uses advanced sensors, microcontroller, GPS and GSM module which can detect and report faults of the railway track. The system uses real time mechanism. The system is implemented and tested on a prototype railway track to detect crack or any fault. The internet of things technology added modern and accurate fault-finding feature in the system. The IoT plays an important role in implementing advance technologies, as it uses sensors and advance microcontroller. The accuracy rate of fault and crack detection is around 97%, which is for 2 mm crack. Along with this GPS module accurately track fault location. GSM module successfully send the alert message quickly after detecting the fault in the track. Hence the response time is very less and accurate. This system is implemented in order to ensure safety for the railway track and prevent the accidents. Hence the system is useful for improving overall transport safety.

Authors

Institutions

Publication Details

Published
2024-11-07
DOI
https://doi.org/10.1109/icitda64560.2024.10809610
Citations
2
Primary Topic
Machine Fault Diagnosis Techniques
Type
article
Field-Weighted Citation Impact
0.66
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Enhancing Railway Safety with an IoT Based System for Real Time Fault Detection and Crack Monitoring

Ravindra Raman Cholla, Rajermani Thinakaran, Vikram Neerugatti, Madhusudana Rao Nalluri et al.
2 citations
Machine Fault Diagnosis Techniques
0.66
article

Enhancing Railway Safety with an IoT Based System for Real Time Fault Detection and Crack Monitoring

Ravindra Raman Cholla, Rajermani Thinakaran, Vikram Neerugatti, Madhusudana Rao Nalluri, Somasekar Jalari, Santhaiah Chukka
article en
2 citations

Abstract

Safety of railway is the major problem worldwide. It has problems like cracks or any fault in the railway tracks. These problems can cause severe accidents if it is not detected regularly and early. In traditional fault finding and crack detecting method, the manual method is involved which is very slow and consist of many errors. By considering all these challenges and issues in track detection method, we have implemented an advanced IoT based railway crack and fault-finding system. It uses advanced sensors, microcontroller, GPS and GSM module which can detect and report faults of the railway track. The system uses real time mechanism. The system is implemented and tested on a prototype railway track to detect crack or any fault. The internet of things technology added modern and accurate fault-finding feature in the system. The IoT plays an important role in implementing advance technologies, as it uses sensors and advance microcontroller. The accuracy rate of fault and crack detection is around 97%, which is for 2 mm crack. Along with this GPS module accurately track fault location. GSM module successfully send the alert message quickly after detecting the fault in the track. Hence the response time is very less and accurate. This system is implemented in order to ensure safety for the railway track and prevent the accidents. Hence the system is useful for improving overall transport safety.

Jain University (IN), INTI International University (MY), ICFAI Foundation for Higher Education (IN)
Climate action
Openalex Percentile: Top 28%
Machine Fault Diagnosis Techniques
0.66
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Enhancing Railway Safety with an IoT Based System for Real Time Fault Detection and Crack Monitoring — Ravindra Raman Cholla, Rajermani Thinakaran, et al. · (2024) | TGRS Research Map | TGRS