# Digitalization and Dynamic Criticality Analysis for Railway Asset Management
## 1 Introduction and Background  
Rail infrastructure maintenance management faces the challenge of ensuring safe and efficient operations in the face of increasing demand for faster and more reliable transport services. As an essential part of the global transport network, rail infrastructure facilitates the movement of people and goods. In another way, digitalization is revolutionizing asset management by improving the accuracy and efficiency of information handling [1]. In the current context, rail asset management faces several challenges:  
* One of the main problems is the reliance on traditional, static methods that rely on past experience and expert judgement, which have shown limitations, especially in adapting to changes and managing unforeseen emergencies. * Integrating legacy systems with new digital tools is another critical challenge. Many rail networks still rely on outdated systems that are not compatible with modern digital technologies. Upgrading these systems requires significant investment and technical expertise, which can be a considerable hurdle for many organizations. * Management of the sheer volume of data generated by IoT devices. These data require robust management capabilities to be useful for strategic and operational decision-making. In addition, the interoperability of maintenance management systems is crucial for effectively implementing digital technologies across the rail network. Achieving this interoperability can be complex. * Cybersecurity has become a critical concern due to increased connectivity and dependence on digital systems. Protecting these systems against cyber threats is essential to ensure security and operational continuity. * Finally, effective implementation of digital technologies remains a challenge, especially in networks with older or less digitized infrastructure. This implementation requires technological, organizational, and training changes to ensure staff adapt to new technologies. Digitalization and criticality analysis together offer innovative solutions to address challenges in railway asset management, sensors embedded in the infrastructure enable the automated collection and processing of real-time data on asset conditions, such as track wear and tear and train conditions. In addition, digital twins, continuously updated with these data, allow operational scenarios to be simulated and potential failures to be foreseen before they occur. * On the one hand, digitalization
* Enables the integration of advanced technologies such as IoT, Big Data, and digital twins, facilitating the collection and processing of real-time data on the condition of railway assets. This not only improves the accuracy of asset condition assessment but also enables a faster and more efficient response to changing conditions and unforeseen emergencies [2]. * Facilitates interoperability and system integration through the use of advanced data models, such as the asset administration shell (AAS), which enables the representation of the digital characteristics of a physical asset. This enables advanced and efficient management throughout the asset lifecycle, overcoming the limitations of legacy systems [3]. * On the other hand, criticality analysis
* Is a methodology that identifies and prioritizes system components whose failure would negatively impact operations. When combined with digitalization, this analysis becomes more dynamic and accurate, allowing the continuous and on-demand assessment of the railway assets. This technique optimizes resource allocation by focusing on the most critical assets, thus improving safety and operational efficiency. In other words, digitalization and criticality analysis address technical and operational challenges and promote a proactive and predictive approach to railway asset management. This approach contributes to increased rail infrastructure reliability and safety, marking a significant advance in asset management in the digital age.