Detection of Railways Through Axle Detection Patterns Using Inductive Proximity Sensors

DOI

10.2991/978-94-6463-384-9_18How to use a DOI?

Keywords

Axle Counter; Detector; Inductive proximity sensor; Arduino Uno

Abstract

Early Warning System (EWS) at level crossing gates is a system used to provide early warning in the form of signals or lights and siren sounds when a train is approaching crossed. This level crossing is prone to accidents. In order to improve railway security and safety, railway facility detectors have an important role in passenger safety nationally. This facility detection system uses an inductive proximity sensor. This vehicle detection system was created to find out what type of equipment has passed and to determine the axle pattern. This tool is designed using hardware and devices soft. The hardware consists of inductive proximity sensors, LCD, I2C, and Personal Computer. Meanwhile, the software consists of Arduino IDE and Visual Basic Studio. In this tool, the sensor is installed parallel to the track with a distance between sensor 1 and sensor 2, namely 60 cm. Based on the tests that have been carried out, this tool can detect the type of passing vehicle which can be determined based on the axle pattern. This sensor can detect well if it is placed at the optimal limit, namely 1 – 4 mm. The output of this type of passing vehicle can be seen on the LCD, while the axle pattern can be seen in the form of a sinusoidal wave via the Visual Basic Studio application. In calculating the average percentage of error on the tool, the percentage result is 0%, which means the tool can work well.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Lusi Ariani
AU  - Fathurrozi Winjaya
AU  - Natriya Faisal Rachman
PY  - 2024
DA  - 2024/02/20
TI  - Detection of Railways Through Axle Detection Patterns Using Inductive Proximity Sensors
BT  - Proceedings of the 2nd International Conference on Railway and Transportation 2023 (ICORT 2023)
PB  - Atlantis Press
SP  - 204
EP  - 212
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-384-9_18
DO  - 10.2991/978-94-6463-384-9_18
ID  - Ariani2024
ER  -