The Application of IoT in Predictive Maintenance for Railway Systems: A Systematic Literature Review

Abstract

This research explores the implementation of IoT-based predictive maintenance within railway systems, focusing on the technologies, cost implications, reliability, safety, and barriers identified in the literature. The study systematically reviews 30 peer-reviewed journals to assess the current state of IoT applications in the railway sector. Critical IoT technologies such as sensors, wireless sensor systems, and edge processing are examined in their role in enhancing predictive maintenance practices. The research highlights significant long-term cost savings associated with IoT adoption, despite high initial implementation costs. Furthermore, the study evaluates how IoT technologies contribute to improved reliability and safety by enabling real-time monitoring and predictive analysis. However, several barriers to widespread adoption are identified, including technical integration challenges, financial constraints, regulatory hurdles, and organisational resistance. The findings underscore the need for a strategic approach that will help tackle all obstacles by realising the benefits of IoT-predictive maintenance in the railway sector. This study offers significant insights for stakeholders, offering a deep understanding of the challenges of IoT-based predictive maintenance in railways. Future research directions are suggested, emphasising the importance of long-term studies, holistic approaches, and the integration of emerging technologies to address the identified barriers.