# Digitalization and Dynamic Criticality Analysis for Railway Asset Management
## 4 Case Study
### Scenario and Application
* Type_Via: The type of track the asset is on can increase or decrease its criticality. For example, an asset on a high-speed track has a higher risk of causing serious problems if it fails, compared to an asset on a low-speed track. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Step** & **Description** & **Source** & **Process** \\ \hline
5 & Station/Path Calculation: Indicates whether the asset is in a station or on a path. & Inventory System & Creation of the station/path field in the master table and calculation based on the start codes. \\
6 & Network Type Calculation: Classifies the type of network (high speed, conventional network, or suburban). & Table of tranches & Creation of the network type field in the master table and calculation based on the coding of the leg. \\  & Track Table Data: Subnetwork Type and Maximum Speed: & Cross-reference the leg table by the leg \\
7 & The subnetwork types and maximum speed are obtained from the track table by cross-reference the track code. & Table of tranches & Code to obtain subnetwork type and maximum speed. \\
8 & Data from the Station Category Table: The station category is extracted by cross-referencing the station code fields. & Table Station & Cross-reference the table by the start station code to obtain the station category. \\
9 & Track Type Table Data: The track type is calculated from the station code field of the track type table. & Table Type of road & Cross-reference the track type by the track code to determine whether it is a main or sliding track. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._Track Device: Devices installed on the track, such as sensors or control devices, also influence criticality. For example, the lack or failure of a safety device could increase the severity. 3. Impact on criticality assessment:  
The figure illustrates how severity levels are calculated based on specific asset attributes. Each combination of attributes generates a particular level of severity that allows corrective actions to be prioritized. Figure 3 provides a clear visual representation of how severity levels are determined based on asset attributes, allowing readers to understand how assets are prioritized for maintenance. By including key factors such as the Class, Type_Via, and Track Device, the figure reinforces the idea that the criticality of assets depends not only on their individual state but also on their context within the rail network. The key results obtained from the criticality analysis in the rail network case study were significant for asset management and optimization. Some of them are summarized as follows:  
* Signaling systems:
* More than 500 systems assessed. * Average frequency of failures: 1.2 incidents per year. * Average delays: 45 min, affecting more than 100 trains and 60,000 passengers daily. * Track devices:
* More than 700 evaluated. * Frequency of failures: 0.4 per year. * Serious consequences: traffic disruption on more than 15 km sections, affecting 20,000 passengers and generating costs of 50,000 euros per disruption. * Electrical substations:
* More than 320 assessed. * Infrequent but critical failures. * Impact: paralysis of sections of up to 50 km, affecting 200,000 passengers and costs of 100,000 euros per hour of inactivity. * Proposal: Real-time monitoring systems will reduce downtime by 40%. * Key for real-time data update. * Digital twins for continuous monitoring and fault simulations. In short, following the methodology proposed by [PERSON] et al. [28], this application resulted in (i) a 25% reduction in unplanned downtime, (ii) improved service availability, (iii) a 15% reduction in operating costs, and (iv) estimated savings of \(\copyright\)2 million in the first year. In other words, we can see how digitalization and criticality analysis transform asset management by enabling prioritization of critical assets and optimization of resources, resulting in improved operational reliability and reduced costs.