# Digitalization and Dynamic Criticality Analysis for Railway Asset Management
## 3 Methodology
### Methodological Development  
* _Description of the ETL process and AAS framework:_ The data model focuses on the process of extracting information from various sources, transforming it to ensure consistency and compatibility, and loading it into a master database that centralizes the data for analysis. This process begins with identifying and collecting operational and performance data from heterogeneous sources. The data are then normalized using relationship tables to create a subset that assigns values to variables for each asset based on asset type, operational context, and the required level of intervention. This approach is reinforced by the principles of the asset administration shell (AAS), which propose a standardized digital representation of the asset. This digital representation acts as a multi-layered structure containing information such as status, service condition, maintenance history, and any other relevant data that aid in developing a dynamic criticality analysis. As an example, Figure 2 represents the structure developed in our use case, where graphically it is possible to appreciate how the multiple attributes are fed for each asset from different sources \"Operational Entities\" through specific relationships. In the same way, from other sources of operational information (MTC), it is possible to establish, by inference from type of asset and reference system, data on the frequency of failures and operational affectation such as delayed trains or unavailability of the system. * _Asset attribute characterization (AAC) and its implementation:_ Asset attribute characterization (AAC) is applied to clearly define the critical attributes that influence the  
Figure 2: Visual representation of the data flows and business rules. criticality of assets. This process includes defining and analyzing variables containing attributes such as failure frequency, unavailability, and other performance indicators that directly affect the operability of railway systems. These attributes characterize the assets, elaborated from the operation (online or on-demand) or history data and integrated into the criticality analysis. * _Procedures for criticality assessment and creation of digital assets_: The procedures established for criticality assessment use algorithms that analyze asset attributes to calculate their potential impact based on predefined criteria. This assessment is visualized in a criticality matrix that segments assets according to risk levels and allows a clear visualization of intervention priorities. Model-specific business rules are executed from the master database values to automatically determine each asset's criticality value. In the previous step, the developer must create the digital asset by replicating the physical and operational attributes of the assets in a standardized digital format. * _Integration with enterprise asset management (EAM) systems_: Integrating criticality scores and risk alerts into the EAM system initiates specific maintenance activities if significant changes in risk occur. For example, measuring the wear of a rail section transmitted by the monitoring train updates the EAM system, allowing the maintenance manager to determine that the section needs to be inspected and repaired promptly, thus avoiding significant problems in train operation. * _Data Visualization and Modeling_: The visualization is used to facilitate understanding of the process and decision-making, following diagrams such as Figure 2, which illustrates the data flow from extraction to criticality assessment. In addition, machine learning algorithms are envisaged to model and predict asset behavior, improving failure anticipation and maintenance strategy. * _Continuous improvement_: Finally, it is suggested that the master data set be enhanced with additional attributes, such as real-time health status measurement, and that advanced analysis techniques be employed to further optimize railway asset management.