Enhancing railway transportation safety with proactive maintenance strategies incorporating machine learning

Recognising the significance of railway infrastructure, effective maintenance is vital to prevent breakdowns, accidents, and ensure smooth operations. This research aims to develop a novel machine learning-based railway predictive maintenance (MLT-RPM) system to address issues of downtime and resource allocation. The system employs sensors to record data on temperature, vibration, and wear, enabling early diagnosis and prevention of locomotive engine failures. This framework enhances railway infrastructure's security and reliability, minimising downtime, costs, and accidents. The study also demonstrates that MLT-RPM reduces energy consumption and environmental impact, promoting safety, dependability, cost savings, operational efficiency, and environmental sustainability.