# Digitalization and Dynamic Criticality Analysis for Railway Asset Management
## 1 Introduction and Background
With the above in mind, the paper develops an approach to railway asset management through digitalization and criticality analysis. To this end, a data model is proposed where through a structured extract, transform, and load (ETL) process and the development of a criticality evaluation algorithm according to this data model, enabling an automated and accurate assessment of the risk associated with railway assets. This paper emphasizes the importance of a holistic approach that combines digital tools with organizational change management as a part of this transversal approach. In addition, it highlights the need for standardization [4] and interoperability to integrate legacy systems with new technologies, thus overcoming the current limitations in asset management. The process aims to optimize resource allocation and improve operational safety through the digitalization of railway maintenance, including data collection and processing, sub-model encapsulation, criticality assessment, and generation of risk alerts. The purpose is to demonstrate that digitalization is a key enabler for managing criticality in complex railway systems, improving reliability, safety, and operational efficiency while addressing technical and organizational challenges. Criticality analysis is essential in managing complex systems, such as rail and utility networks, to identify and prioritize elements whose failure could negatively impact operations [5]. This analysis focuses on tracks,signaling systems and control units, critical safety, and operational efficiency components in rail networks. In this context, [PERSON] et al. [6] develop the application of operational characteristics of the asset (OCA) like a vector of attributes representing a list of properties that describe the asset. In the railway case, the values of this vector are the different attributes that affect the performance or criticality of the asset. Each attribute (a1, a2,..., an) describes a specific operational characteristic of the railway asset. Examples of operational attributes are as follows:  
* Location of the asset (on a journey or at a station): This attribute tells you whether the asset is in a train station or along a journey between stations. Location can influence the frequency of failures due to wear and tear and the consequences of failures, as a failure at a station could have a different impact than a failure at a track. * Network type (high-speed, conventional, suburban): Depending on the network, assets may be subject to different voltage levels, speeds, and usage, affecting the likelihood of failure and its consequences. For example, a high-speed network can have more severe consequences if it fails. * Traffic speed: Assets located on sections where trains run at higher speeds may be subjected to more significant mechanical stress, increasing failure frequency. In addition, if a failure occurs, the consequences could be more severe due to the high speed. * Asset location: Assets in curves or tunnels may have more adverse conditions affecting their durability. In a curve, trains can generate greater friction, and in a tunnel, ventilation and humidity conditions can be different, affecting both the frequency of failures and the consequences of those failures (e.g., longer or more difficult-to-repair service interruptions). * Impact on failure frequency: Each attribute can increase or decrease the likelihood of the asset failing. For example, a curve or tunnel asset might fail more often than one in a straight, open section due to more demanding operating conditions. * Impact on the consequences of failures: In addition to frequency, these attributes affect the consequences when the asset fails. A failure in a tunnel can be more challenging to repair and have more severe consequences than one in an open area. Similarly, a failure in a high-speed network could cause more severe delays than in a suburban network. This analysis allows asset managers to prioritize resources towards the most critical elements, improving system safety and reliability [7].