Intelligent Railway Wagon Health Assessment Using IoT Sensors and Predictive Analytics for Safety-Critical Applications

The safety and reliability of railway wagon operations largely depend on the timely detection of degradation in safety-critical components such as axle bearings, wheelsets, and braking systems. Conventional maintenance strategies based on fixed inspection intervals are often inadequate for capturing the actual operating conditions of wagon components, leading to delayed fault detection or unnecessary maintenance actions. To address these limitations, this paper proposes a sensor-based health assessment framework for the continuous monitoring of railway wagons under operational conditions. The proposed framework integrates multi-sensor data acquisition, systematic signal preprocessing, feature-based health indicator construction, and temporal degradation analysis to evaluate component health in real time. A safety-oriented decision logic is employed to classify operating conditions and generate reliable alerts while minimizing false detections caused by transient disturbances. The effectiveness of the proposed approach is validated using a publicly available run-to-failure bearing dataset that exhibits degradation characteristics similar to those observed in railway wagon axle bearings. Experimental results demonstrate that the proposed framework achieves improved classification accuracy, higher detection reliability, reduced false alarm rates, and lower detection latency compared to representative existing condition monitoring approaches. In addition, the computational efficiency of the proposed model confirms its suitability for real-time deployment. The results indicate that the proposed health assessment framework provides a practical and reliable solution for safety-critical railway wagon monitoring and forms a strong foundation for future extensions toward predictive maintenance and remaining useful life estimation.