Driss El Hor

Predictive maintenance has become an important strategy for improving asset reliability and safety, particularly with the increasing integration of artificial intelligence (AI) into maintenance processes. In the railway domain, where maintenance activities can account for up to 40% of the overall budget allocated across the V-cycle of rolling stock development, the adoption of smart maintenance strategies is increasingly important. However, an integrated understanding of predictive maintenance approaches across the railway domain remains lacking. This study provides an in-depth review of predictive maintenance research in the railway domain through a PRISMA-guided systematic search of three major scientific databases: Scopus, Web of Science, and ScienceDirect. A total of 61 full-text articles were retained and analyzed in detail. The reviewed studies are synthesized according to their methodological approaches, namely data-driven, model-based, and hybrid approaches, and their distribution across railway subsystems is examined. The analysis reveals a concentration of research on rolling-stock components, particularly wheelsets and switch machines, while comparatively less attention is devoted to less-instrumented but similarly safety-critical assets, such as traction, signaling, and door systems. Based on these findings, a conceptual hybrid predictive maintenance framework is proposed by synthesizing the approaches identified in the literature. Finally, the study highlights key research challenges and identifies open research questions and future research directions, including the need for more comprehensive, subsystem-specific, and practically validated predictive maintenance solutions.

Predictive maintenance has emerged as a key strategic approach across numerous industrial sectors, particularly in the current context marked by the systematic integration of artificial intel-ligence (AI) technologies into asset management processes. Within the railway domain, maintenance plays a critical role in ensuring both operational reliability and safety, with maintenance-related activities accounting for up to 40% of the overall budget allocated across the V-cycle of rolling stock development. Given the essential contribution of rail transport to sus-tainable mobility and to the movement of goods and passengers, the demand for intelligent and efficient maintenance strategies continues to grow. However, a comprehensive predictive maintenance framework tailored to the railway sector remains lacking. This study presents an in-depth review of the principal methods employed in recent years, combining a PRISMA-guided systematic search of three major scientific databases Scopus, Web of Science, and ScienceDirect. A total of 61 full-text articles were retained and analyzed in detail. The paper synthesizes current research trends, frameworks, and algorithms, categorized into data-driven, model-based, and hybrid approaches, and examines their distribution across railway subsystems, revealing a con-centration of research effort on rolling-stock components such as wheelsets and switch machines relative to less-instrumented, comparably safety-critical assets such as traction, signaling, and door systems. Building on these insights, a conceptual hybrid predictive maintenance framework is proposed as a synthesis of the reviewed approaches. Finally, the paper discusses the key challenges identified in the literature and outlines open research questions with potential direc-tions for future work.

In the contemporary academic and industrial contexts, predictive maintenance has emerged as a pivotal concern, particularly in the era of artificial intelligence's integration into maintenance operations. This technique promises notable benefits, including cost savings, increased component remaining useful life, enhanced system availability, and the capability to monitor operations in real time. There are three primary categories of predictive maintenance: the data-driven approach, the model-based method and the hybrid model. In railway maintenance play a pivotal role to avoid line interruption and ensure high rolling stock availability. This article provides a comprehensive review underscoring the main methods utilized in recent years thorough a deep analysis of 599 relevant articles obtained from scientific databases. PRISMA method was used to scan relevant studies in the last two decades in three popular databases: Scopus, Web of science and Science Direct. Accordingly, the study presents a survey of predictive maintenance frameworks, approaches and algorithms, in which data driven approach using machine and deep learning techniques were widely employed. Also, it shed light on the major rail equipment that was the focus of the researches. To harness the complementary strengths of both data-driven and model-based, we have proposed a novel hybrid framework that synergistically integrates real-time analytics with theoretical modeling. In conclusion, limitations and challenges were introduced with potential improvements and perspectives for future work.