Data Driven Predictive Maintenance Framework for Railway Safety in Indonesia - Pasurangga, Dimas; Baltasar, Sora (ADI Journal on Recent Innovation, 2025)
Indonesia’s railway network faces increasing operational pressures as passenger and freight volumes continue to rise, revealing the limitations of reactive maintenance approaches and emphasizing the need for predictive, data driven safety mechanisms. This study aims to develop a conceptual framework for a data-driven predictive maintenance system to enhance railway safety, reliability, and operational efficiency in Indonesia. A Systematic Literature Review (SLR) was conducted on international and national studies presents 25 key references published between 2016 and 2025 on early detection systems in railway, focusing on railway condition monitoring, IoT based maintenance, and AI driven safety analytics. The synthesized findings indicate that integrating IoT sensors, vibration monitoring, and hot box and hot axle detection systems supported by artificial intelligence and big data analytics can significantly improve early anomaly detection, predictive decision-making, and risk prevention. Nevertheless, several challenges remain, including limited technical capacity, fragmented regulations, and high implementation costs. The proposed data driven predictive maintenance framework positions early detection systems as strategic instruments for digital transformation in railway operations, strengthening risk management, promoting sustainable infrastructure, and aligning Indonesia’s railway governance with global standards for intelligent and resilient transportation systems.