◆ 참여자: 고권표, Chaudhuri Simoni, Abbosjonov Saidakbar Umarjon Ugli, Yusupov Komiljon Rasuljon Ugli, Kaung Mon Thant

◆ 개발기간: 2024.9~2024.12

◆ 프로젝트 내용: An analysis of failures in railway signalling control systems from 2000 to 2010 shows that track switcher failures account for 27%. Most failures were caused by small electrical and mechanical faults rather than external factors. Limited data-driven insights for predictive maintenance hindered operators from predicting possible failures of point switch machines. To address this issue, this project introduces system, which detect anomalies in the patterns given from the point switch machines in real time by using a machine learning predictive analysis model to identify failures before accidents occur to ensure system safety. 

This project aims to reduce railway accidents by analyzing data from point switch machines. Predictive analysis of voltage and current readings will identify potential failures and schedule maintenance, enhancing safety and reliability.