Improving railcar condition monitoring effectiveness is highlighted as a means to reduce in-service failures, and operational variability in rail operations. Emphasizing the significant impact of railcar condition on operational reliability, safety, and efficiency, the current manual inspection practices are noted to be limited in enabling anticipatory maintenance. To address this limitation and facilitate predictive maintenance, automated condition monitoring technologies like On Train Data Recorders (OTDR) have emerged. The emerging intelligent data storage technologies underscore the importance of data analytics and machine learning methods in enhancing railcar maintenance practices and service inspection efficiency. The research aims to enhance predictive maintenance strategy within rail systems by overcoming data exchange challenges and semantic heterogeneity. The study explores Semantic Web and Linked Data techniques in the railcar cyber-physical subdomain systems. Findings from the study indicate significant main-line causes compared to rail equipment downtime and damages. This endeavour contributes to the development of a robust operational reliability framework in minimizing service disruptions within rail systems.