Predictive Maintenance (PM) represents a transformative approach applied in industry and, naturally, within the transportation sector, optimizing infrastructure functionality and resilience, while minimizing resources and costs. Diverging from traditional maintenance strategies, such as reactive and corrective methods, PM employs digital technologies—Edge and Cloud Computing, Internet of Things (IoT), Artificial Intelligence (AI)—to proactively anticipate and prevent potential failures, ensuring continuous operational efficiency, an essential factor in transportation industry.
This is crucial for further reducing operational expenses (OPEX), while avoiding unexpected losses and enhancing system reliability through multi-objective optimization. Moreover, PM’s integration of reporting models, structural assessment methods, forecasting systems and remote infrastructure management forms a comprehensive framework that supports data-driven decision-making, early defect detection, predictive analysis and real-time health monitoring of transport infrastructure. These advancements not only contribute to material and energy savings, but also support the efforts to mitigate climate change effects, positioning PM as a milestone of progress and sustainability.
This paper presents an innovative integration of disciplines and specialties applied to the creation of holistic familiarization content on Predictive Maintenance, adapted to meet contemporary requirements and hence, demonstrating its critical role in the evolution of maintenance methodologies and its impact on transportation sector’s future. It is structured to cover the theoretical framework (comparison with traditional maintenance and key technologies), applications (examples of PM application in transportation), benefits (financial and environmental advantages, increased system reliability and safety), challenges (technical and financial), future directions and solutions to overcome existing limitations of PM, concerning the area of modern transportation.