Data analytics accessibility and predictability in Singapore’s rail sector

• Main Author: Chia, Ethan Cheng Wai
• Other Authors: Ling Keck Voon
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2024
• Subjects: Engineering; Predictive maintenance; Railway; Rolling stock; Signalling; REAMS; Land Transport Authority; Communications
• Online Access: https://hdl.handle.net/10356/177099
• Institution: Nanyang Technological University

This Final Year Project (FYP) evaluates the accessibility of performing data analysis in Singapore’s rail network, yet not compromising on the data-driven experience. Singapore’s rail network has a plethora of rail systems that host their own database of logs, comprising of Events and Alarms. Maintainers and Engineers would need to login to these systems individually to retrieve them. During the subsequent log analysis, engineers are overwhelmed with the comprehensive logs provided and need to cross-reference across different files, dates or even logs of other systems. Each working department will have their own working space and practices on how data is stored, retrieved, and manipulated for analysis. This report documents the investigative journey undertaken to unify some of the manual data process and demonstrate predictive data analysis. There are two distinct parts to answer. Firstly, can we produce a data-driven space that is user-friendly and flexible for any user to use? Secondly, can this space permit quality findings in predictive data analysis?