Signal Lamp Damage Detection System at Train Stations Using Predictive Maintenance Method
Andriana, Ida Hamidah, Tutin Aryanti, Zulkarnain, Heru Prambudiono

Universitas Langlangbuana (UNLA) Bandung
Universitas Pendidikan Indonesia (UPI) Bandung

Abstract

Signal lights play an important role in the railway system, therefore signal lights must be detected if they are not functioning normally. The signal light signal is red light which means unsafe, yellow light means walking cautiously and green light means traveling at a specified speed. To overcome this, the authors made a signal light system that can detect if the lamp is damaged, and can also predict if the signal lamp has the potential to be damaged with a customized predictive maintenance method that accurately predicts the problem. The components used for detection use the CZ3700 and INA219 sensors which can detect 0 to5A currents and 0 to 36 Vdc voltages in signal lamps. In addition, it is also equipped with a temperature sensor to measure the temperature in the signal light module, which can read temperatures from 0 to 85 degrees Celsius. The sensor readings are processed using the Arduino ESP32, and the readings are sent to the Human Machine Interface, in this case using a Computer via a wireless network. The hope is that when you have implemented this predictive maintenance system it can identify and fix problems before the signal lights are damaged.

Keywords: Sensor CZ3700, INA219, Arduino ESP32, Human Machine Interface, Predictive Maintenance

Topic: Engineering and Technology