# Digitalization and Dynamic Criticality Analysis for Railway Asset Management
## 3 Methodology
### Proposed Procedure  
In the context of railway infrastructure maintenance, this study applies a digitalized process of criticality analysis, as detailed in the case study in Section 4. This process involves the implementation of a customized data model to enhance maintenance decision-making. The criticality-based maintenance model is specifically applied to a railway track system, where maintenance managers must prioritize activities based on the importance rating of the equipment. In the following sections, the steps of this approach are implemented:  
Figure 3: Example of a rules for a severity factor (Safety).  
Data Collection: The first phase consists of obtaining updated data on demand from different points, e.g., tracking maintenance history, asset classification, asset inventory, section criteria, operational definitions, and others.
2. Data processing: After collecting the data, they are processed and converted to a commonly understandable format according to each asset type's respective business rules, considering that the railway system has a heterogeneous asset typology. In this sense, each asset type has a specific rule and sphere of influence. In this step, we define the relationship between each operational entity and several relationship tables to prepare the query that will provide the attribute values for each asset.
3. Sub-model encapsulation: Within the context of AAS, these processed data are encoded into sub-models, allowing for a current digital reproduction of each asset's state in time. To avoid manual data processing, we attach to each asset a shell that collects the entities and assigns them a model of inputs and outputs that can then be automatically run to obtain the normalized information on demand.
4. Criticality Assessment: Sophisticated algorithms with many rules process the data, and scores are determined for each asset. The above scores indicate how vital the security effects, performance, and cost impacts on the given assets will be.
5. Risk Alerts and Dashboard: With up-to-date information, the system can automatically produce risk alerts on highly critical assets based on their criticality scores, which are displayed on an easy-to-understand dashboard. It helps maintenance managers decide which assets should be prioritized for maintenance. In the case of criticality assessment, real-time data are not necessary because the objective is a strategic assessment. Still, data availability enables the system to apply condition-based techniques or health monitoring tools, of course, with other output models.
6. Integration with the EAM System: This integrates criticality scores and risk alerts into the EAM system, initiating specific maintenance activities if major changes in risk occur.  
For example, we can analyze a part of a railway track that has degraded at different levels. This wear is measured by the monitoring train that transmits data on the new condition of the track segment. The data model receives these data, processes and analyses it with updated data, and is available for any engineer to demand updated data to reassess the criticality of the line. In addition, these data can be contained in AAS, which scores critically high for some track lengths due to abnormal patterns and large deviations in the segment in question [27]. The system issues a risk alert, which can be viewed on the maintenance control panel. After reviewing the panel in the EAM, the maintenance manager determined that the section above must be inspected and repaired promptly, as it presents a significant threat to the operation of the train. This decision updates the EAM system and dispatches a maintenance team to address the problem promptly, thus preventing potential incidents.