IoT Remote Monitoring and Maintenance Strategy for Key Components of High-Speed Train Vehicles

High-Speed Trains (HSTs) are important to contemporary transport, and the dependability of their key components is critical to ensuring operational security and efficiency. Traditional maintenance procedures usually rely on regular inspections that fail to address unexpected problems in real time, resulting in costly repairs and operational interruptions. The major goal is to improve the dependability and efficiency of HST vehicles by using Internet of Things (IoT)-based remote monitoring systems to predict repairs, reduce downtime, and improve safety. The proposed system collects real-time operational data using IoT sensors integrated into important train components (e.g., wheels, brakes, engines, and tracks). The data are pre-processed via min-max normalization, and the features are extracted using the Fast Fourier Transform (FFT). Predictive maintenance algorithms, which use Intelligent White Shark Optimized Scalable Support Vector Machine (IWSO-ScaleSVM) techniques, evaluate data to predict probable breakdowns and send alerts. The system continuously checks component status and sends real-time signals to maintenance staff, allowing for timely actions. The suggested IoT-based system accurately predicts component failures, decreasing unscheduled maintenance and increasing component durability. The integration of IoT technology with predictive maintenance marks a significant step forward in the remote monitoring and maintenance of HST components. The proposed model is assessed on various types of metrics, including F1-score (98.51%), recall (98.51%), accuracy (98.55%), and precision (98.52%). The technology improves operating efficiency, reduces downtime, and provides safe and cost-effective train fleet management.