Digitalization and Dynamic Criticality Analysis for Railway Asset Management

Citation: Hernández, M.R.; Sánchez-Herguedas, A.; González-Prida, V.; Contreras, S.S.; Crespo Márquez, A. Digitalization and Dynamic Criticality Analysis for Railway Asset Management. Appl. Sci. 2024,14, 10642. https://doi.org/ 10.3390/app142210642 Academic Editor: Arkadiusz Gola Received: 12 October 2024 Revised: 5 November 2024 Accepted: 15 November 2024 Published: 18 November 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Article Digitalization and Dynamic Criticality Analysis for Railway Asset Management Mauricio Rodríguez Hernández 1, Antonio Sánchez-Herguedas 1, Vicente González-Prida 1,* , Sebastián Soto Contreras 2and Adolfo Crespo Márquez 1 1Higher Technical School of Engineering, University of Seville, 41092 Seville, Spain; [email protected] (M.R.H.); [email protected] (A.S.-H.); [email protected] (A.C.M.) 2Smart Industry Engineering, Pontifical Catholic University of Valparaíso, Valparaíso 2340025, Chile; [email protected].cl *Correspondence: [email protected] Abstract: The primary aim of this paper is to support the optimization of asset management in railway infrastructure through digitalization and criticality analysis. It addresses the current challenges in railway infrastructure management, where data-driven decision making and automation are key for effective resource allocation. The paper presents a methodology that emphasizes the development of a robust data model for criticality analysis, along with the advantages of integrating advanced digital tools. A master table is designed to rank assets and automatically calculate criticality through a novel asset attribute characterization (AAC) process. Digitalization facilitates dynamic, on-demand criticality assessments, which are essential in managing complex networks. The study also underscores the importance of combining digital technology adoption with organizational change management. The data process and structure proposed can be viewed as an ontological framework adaptable to various contexts, enabling more informed and efficient asset ranking decisions. This methodology is derived from its application to a metropolitan railway network, where thousands of assets were evaluated, providing a practical approach for conducting criticality assessments in a digitized environment. Keywords: criticality analysis; data model; digitalization; digital twin; extract, transform, and load (ETL); asset attribute characterization (AAC); railway maintenance 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. Appl. Sci. 2024,14, 10642. https://doi.org/10.3390/app142210642 https://www.mdpi.com/journal/applsci Appl. Sci. 2024,14, 10642 2 of 24 • 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 ondemand 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. 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, Appl. Sci. 2024,14, 10642 3 of 24 signaling systems and control units, critical safety, and operational efficiency components in rail networks. In this context, Rodriguez 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 ]. The technique facilitates proactive maintenance planning, reducing unexpected failures and ensuring the continued operability of the network. A failure in any of these can disrupt service and compromise security. Criticality analysis identifies vulnerable elements and prioritizes their maintenance, minimizing risks of unforeseen failures [8]. In other networks, complex network analysis (CNA) and graph Fourier transform (GFT) are applied to optimize asset management and enhance criticality assessment [ 9 , 10 ]. Additionally, machine learning techniques are employed to predict failures and detect anomalies [ 11 ]. Each network’s unique characteristics influence the selection of criticality analysis models and the corresponding data. Table 1provides a summary of these network characteristics. While both rail and utility networks use criticality analysis methodologies and advanced digital tools, there are vital differences in the criteria and approaches used to assess criticality. In Table 2, some of the “Key elements” that are considered in this context are reviewed and compared, highlighting the differences between one type of network and another. Criticality analysis in rail and service networks shows that although the methodologies and technologies used for analysis may be similar, the objectives and criteria for each system vary significantly depending on the nature of the network. In rail networks, the focus is on operational safety and service continuity, where the failure of critical components can directly impact passenger safety and system operation, unlike utility networks that prioritize resilience and quality of service, focusing on ensuring continuity of supply. Digitalization, including technologies such as digital twins and predictive analytics, plays Appl. Sci. 2024,14, 10642 4 of 24 a key role in both contexts, enabling greater efficiency in maintenance planning and asset management. However, the challenge remains in the effective implementation of these technologies, especially in utility networks with older or less digitized infrastructure. Table 1. Summary Table of Characteristics and Data Used in Criticality Analysis by Network Type. Features Water Network Gas Network Electricity Grid Teleco Network Main Asset Reference Piping and valves Pipelines and compressor stations Transformers and transmission lines Routers and switches Expression of Risk in Terms of Value Impact on drinking water supply Impact on gas distribution and safety Impact on the stability of electricity supply Impact on service quality and reputation Semi-quantitative method Probability of failures and consequences in terms of service interruption Likelihood of leakage and economic consequences Probability of failures at critical nodes and consequences in terms of outages Probability of node failures and consequences in terms of loss of connectivity Business Value Factors Reliability and availability of water supply Security and continuity of gas supply Reliability and energy efficiency Quality of service and SLA compliance Probability and Consequences of Failure Historical failure analysis and impact simulations Incident analysis and impact simulations Failure models and cascade simulations Historical failure analysis and impact simulations Severity Distributions and Frequency Failure histograms Incident histograms Fault distributions Failure histograms and criticality analysis Table 2. Comparison between rail and utility network key element for criticality analysis. Key Element for Criticality Analysis Railway Networks Utility Networks (Water, Gas, Electricity, Telecommunications) Critical Components Tracks, signaling systems, trains, control units Pipelines, valves, pipelines, compressor stations, transformers Criticality Criteria Operational security, continuity of service, infrastructure failure Security of supply, resilience, quality of service Methodologies Failure mode and effects analysis (FMEA), Fault tree analysis (FTA), digital twins Risk-based corrective action (RBCA), FMEA, simulations based on historical data analysis Digital Technologies Real-time monitoring, failure prediction with digital twins Predictive analytics, maintenance automation Impact of Failures Service interruptions, delays, security impact Loss of supply, impact on quality of service, economic and safety risk Optimization Objectives Minimization of failures, optimization of maintenance Improved resilience, reduced operating costs, customer satisfaction The objective of this research is to develop a digitalized data model that enables more accurate and real-time criticality analysis in railway maintenance management. By integrating an ETL process and advanced technologies such as digital twins and asset attribute characterization (AAC), this study aims to optimize the identification and prioritization of critical assets, thereby enhancing operational efficiency, resource allocation, and safety. Section 2presents a comprehensive review of the relevant literature to establish the theoretical framework for the proposed model, drawing on recent advancements in digitalization and predictive maintenance. The methodology is developed in Section 3, including the ETL process and the model for criticality assessment. A case study focusing on a commuter rail network is shown in Section 4. In Section 5, the results are analyzed, and the practical implications are discussed, with recommendations for future research. Finally, the conclusions are exposed in Section 6. 2. Literature Review The theories and concepts related to digitalization and criticality analysis in railway maintenance focus on transforming traditional practices towards more advanced and efficient approaches through digital technologies. Relevant studies such as [ 12 , 13 ] follow this line. These concepts and theories are fundamental to understanding how digitalization can improve railway asset management, enabling more efficient and safer maintenance Appl. Sci. 2024,14, 10642 5 of 24 practices. The previous research has explored how digital technologies can transform and optimize railway maintenance. • Crespo Márquez [ 3 ] proposes a model incorporating digitalization as a crucial enabling factor, facilitating extensive assessments in organizations managing millions of assets. • The Asset Management Working Group [ 4 ], corresponding to the International Union of Railways (UIC) guidance, suggests a model capable of managing risk throughout the asset lifecycle, integrating operations and maintenance management. This approach implies a high degree of complexity in handling multiple data sources, which makes the implementation of advanced technologies such as digital twins essential. • Digital twin studies in [ 14 , 15 ] explore using digital twins to improve railway asset management. These twins allow real-time simulation and analysis of asset behavior, improving criticality analysis and enabling a more proactive approach to maintenance. • Research on IoT and Big Data in rail asset management has been extensively studied in references such as [ 16 , 17 ], highlighting how these technologies enable real-time data collection and analysis to optimize decision-making and improve operational efficiency. • The use of artificial intelligence (AI) in railway maintenance has shown substantial advantages, especially in identifying and predicting defects using techniques such as computer vision and machine learning [ 18 ]. For example, automated visual track inspection allows rail geometry defects and component wear to be detected with significantly higher accuracy than traditional manual inspections. In addition, deep learning algorithms can detect objects on roads and segment signals in complex environments, improving safety and reducing the risk of accidents. However, the deployment of AI in this sector faces critical challenges. One of the biggest challenges is the lack of specific standards and regulations that ensure the safety and reliability of AI-based systems. In addition, AI integration requires a large amount of high-quality data, the availability and accuracy of which can vary considerably between railway systems. Despite these challenges [ 19 ], AI has the potential to transform railway maintenance management, enabling real-time scenario simulation and predictive alert generation that improve operational efficiency and safety. • Several studies, shown in Table 3, have laid the groundwork for implementing digital technologies in rail asset management, demonstrating their potential to improve the safety, efficiency, and sustainability of rail operations. The current literature on the digitalization and management of railway assets has identified several gaps that limit the full exploitation of digital technologies in this sector. # Heterogeneous Data Integration: One of the main challenges is integrating extensive volumes of data from various sources, such as sensors and monitoring devices. Consistency in analyzing these data remains a complex task due to railway systems’ dynamic and varied nature. # Scalability and Flexibility: Another critical gap is ensuring that digital solutions are scalable and flexible to adapt to different railway environments. While promising models have been proposed, such as digital twins, more studies are needed to explore their scalability in various operational contexts. # Interoperability and Standardization: A significant obstacle is a lack of standardization and interoperability between different technologies and systems. Both aspects hinder the effective integration of new technologies with legacy systems, which is crucial for efficient asset lifecycle management. # Application of Advanced Data Models: Although progress has been made in collecting and analyzing large volumes of data, few studies have succeeded in integrating these data into operational models that facilitate dynamic criticality assessment and real-time predictions. # Cybersecurity and Data Management: Secure data management and protection against cyber threats are growing concerns as more aspects of railway maintenance are digitized. Addressing these challenges is essential to maximize the potential of digitalization. Appl. Sci. 2024,14, 10642 6 of 24 These gaps highlight the need for further research and development of solutions that enable more effective and secure integration of digital technologies in rail asset management. The table shows challenges and gaps in the literature (Table 3). Table 3. Literature Gaps and Challenges. Gap Identified Detail References Holistic Integration Need to combine various digital tools into a unified framework. [20,21] Use of Real-Time Data Development of methodologies for using real-time data in dynamic criticality analysis. [17,22] Standardization and Interoperability Development of common standards and protocols for the seamless integration of digital technologies. [20] Human and Organizational Factors Addressing the cultural and structural changes necessary for digital transformation. [23,24] Cybersecurity and Data Privacy Ensuring the security and integrity of data in digital systems. [25] Cost Benefit Analysis Conducting comprehensive cost-benefit analyses of digitalization. [3] Scalability and Flexibility Development of scalable and flexible digital solutions to adapt to different contexts. [14] This study is positioned as a significant contribution to the railway digitalization and asset management field, addressing several gaps identified in the previous research. One of the main contributions is creating an innovative data model that efficiently integrates digitalization in the criticality analysis of railway systems. This integration differs from previous research that has dealt with digitalization in a more fragmented way, without a clear ontological framework for criticality assessment. Furthermore, this contribution addresses the need for advanced methodologies to use real-time data, a gap highlighted by other researchers, such as [ 17 , 22 ], who failed to integrate these data into a dynamic criticality framework. This study seeks to close existing gaps by providing a practical framework for criticality assessment through digitization. 3. Methodology This research proposes an extract, transform, and load) process to develop a data model that facilitates the digitalization of criticality analysis in railway maintenance management through the application of automated rules based on the inherent attributes of each asset. This approach creates an ontological framework that establishes the foundation for replicating this type of analysis across other railway networks. Unlike traditional methods, which rely on static information and expert judgment, our methodology addresses the need for a system capable of managing large-scale asset contexts and diverse operational environments by automating criticality assessment and enabling real-time data integration and analysis. Figure 1provides a graphical representation of this framework and illustrates how its different elements interact, offering a comprehensive view of the process, which is further detailed throughout this paper. Appl. Sci. 2024,14, 10642 7 of 24 Appl. Sci. 2024, 14, x FOR PEER REVIEW 7 of 25 Figure 1. Framework for the Criticality Analysis in Railway. Figure 1. Framework for the Criticality Analysis in Railway. Appl. Sci. 2024,14, 10642 8 of 24 3.1. Methodological Development a. 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 2represents 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. Appl. Sci. 2024, 14, x FOR PEER REVIEW 8 of 25 3.1. Methodological Development a. 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. Figure 2. Visual representation of the data flows and business rules. Figure 2. Visual representation of the data flows and business rules. b. Asset attribute characterization (AAC) and its implementation: Asset attribute characterization (AAC) is applied to clearly define the critical attributes that influence the Appl. Sci. 2024,14, 10642 9 of 24 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. c. 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. d. 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. e. 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. f. 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. This methodology not only proposes an effective operational model but also sets a precedent for future improvements in digitalization and criticality analysis. 3.2. ETL Process for Rail Asset Assessment The extract, transform, and load (ETL) process is crucial for efficiently handling and processing large volumes of data in railway infrastructure systems, ensuring that raw data from various sources can be transformed into actionable insights. In this section, we describe the key variables and steps involved in the ETL process, which supports the criticality assessment model. Section 3.2.1 defines the main variables—such as raw data, transformations, and the final load—that are used to standardize and structure the data for analysis. Section 3.2.2 details how the ETL process is implemented in practice, including extraction from multiple sources, transformation through normalization, filtering, and attribute creation, and the final load into a master table. Finally, Section 3.2.3 formalizes the complete ETL process, presenting the mathematical model used to integrate these steps and how it can be adapted to other railway systems. 3.2.1. Definition of Key Variables in the ETL Process This section outlines the essential variables involved in the ETL process, including raw data, the transformations applied to it, and the final load into a centralized database. These variables form the basis of the data model that supports criticality assessment for railway assets •Raw Data (D): - (D) represents raw data extracted from various sources, such as Excel files, SQL databases, inventory systems, and real-time IoT sensors. - This data is organized in several tables (D1, D2 . . . ,Dn) where each table contains asset-specific information (e.g., it could contain inventory information, failure Appl. Sci. 2024,14, 10642 16 of 24 process to select 13,000 relevant records for the study. However, the descriptions of the failures were performed in text (no code), which made a more in-depth analysis of the failure modes challenging. Clear criteria allowed the derivation of meaningful values at the element level for each section of the assessed network. One of the most significant challenges was the lack of accurate data on maintenance costs in the enterprise resource planning) system, which limited the analysis in that specific dimension. A detailed process of creating and populating the data master (master table) was implemented to facilitate the criticality analysis, serving as the central asset assessment database. Specifically, the data model and the master table were applied to promote a comprehensive analysis of the criticality of railway assets: • The data model provided an organized structure for capturing and analyzing detailed information about each asset, including attributes such as type, location, current status, and maintenance history. This structure was essential to understanding the relationships between the different components of the network and how the failure of one component could affect others. • The master table was created as a centralized database that integrated data from various sources, such as inventories and maintenance records, through SQL queries. This process allowed the extraction and combination of crucial data, such as failure frequency and asset classes, resulting in a rich and detailed database for criticality assessment. Using the master table allowed for automating the calculation of asset criticality, assigning a specific value to each asset based on its characteristics and operational context. This automatization was achieved by integrating various relationship tables that identified the levels of intervention required for each type of asset. In addition, cross-referencing with other key tables was performed to obtain specific characteristics, such as network type and station category, which were essential to associate attributes with specific network assets correctly. In other words, the data model and the master table facilitated a more dynamic and effective criticality analysis, allowing the prioritization of interventions and asset management optimization in the rail network. Specific data covering various dimensions of the railway system were used in the practical application. These data included track maintenance history, asset classification, asset inventory, section criteria, and operational definitions. These data were collected on demand from different points, ensuring the information was up-to-date and relevant to the criticality analysis (Table 5). The data collection process started by obtaining information from various sources, such as maintenance records, asset inventories, and sensors installed on the railway infrastructure. These data were transformed and standardized to ensure their consistency and usefulness, converting them into standard formats that the asset management system could use. Table 5. Detailed Steps for the Creation and Population of the Master Board. Step Description Source Process 1 Import Tables: The information on the maintainable assets shall be provided in spreadsheets. The information necessary to calculate the criticality will be extracted from these tables. An outline of the relationships between the tables and the final structure of the master table is presented. Spreadsheets Extraction of data from various tables. Creation of the master table structure. 2 Initial Master Table Load: Import existing data into the inventory table to determine which assets are maintainable. Generate a new relationship table to identify the maintainable assets. Inventory System Generation of a relationship table to identify the maintainable assets in the inventory table. 3 Relationship Table: Specialties generate tables to provide information on maintainable assets and their selected level of intervention. Inventory system, technical structures Creation of the relationship table for specialities, obtaining information on maintainable assets and intervention levels. 4 Database queries: Execute database queries using SQL to integrate the system’s base tables, such as inventory, attribute source, and relationship tables. This execution will allow the final extraction of assets, asset classes, failure frequency, and attributes. SQL Database Integration of various tables through SQL queries to obtain final asset data and required attributes. Appl. Sci. 2024,14, 10642 17 of 24 Table 5. Cont. Step Description Source Process 5Station/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. 6Network 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. 7 Track Table Data: Subnetwork Type and Maximum Speed: The subnetwork types and maximum speed are obtained from the track table by cross-referencing the track code. Table of tranches Cross-reference the leg table by the leg code to obtain subnetwork type and maximum speed. 8Data from the Station Category Table: The station category is extracted by cross-referencing the station code fields. Table Station Category Cross-reference the table by the start station code to obtain the station category. 9Track 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 siding track. Similarly, data capture would be carried out for the rest of the characteristics, such as: •Type of substation •Slope height and distance •Maximum altitude (altitude) •Type of high-voltage line •If the maintainable asset is located in a tunnel •If the maintainable asset is on a curve •If the maintainable asset is located in an environmentally protected area •If the maintainable asset is at an upper step •Unavailability time (UT) and failure frequency (FF) for each station or route •Average hourly traffic Data collection was a comprehensive process that involved obtaining information from multiple sources, transforming it into standard formats, and integrating real-time data for a more dynamic and accurate analysis of railway assets’ criticality. Once the data were collected and linked, customized business rules were implemented for each type of asset in railway systems. An example of the rules applied is shown in Figure 3, where four levels of severity are defined: low, half, high, and inadmissible, specifically for one of the safety factors. In this case, the severity is determined based on the value of multiple attributes of the assets, such as • Class: Classification of the asset according to its function or criticality in the railway network. • Type_Via: The type of track where the asset is located, which can influence criticality and the risk of failure. • Track Device: Track-installed devices that can modify the behavior of the asset or influence its probability of failure. 1. Severity levels: # The figure classifies the different conditions of the assets according to their criticality, using a severity scale. This multi-tiered approach allows maintenance or mitigation actions to be prioritized based on the associated risk. # A low level of severity implies that the asset presents a low security risk, while an inadmissible level would indicate a high risk that needs to be addressed urgently . 2. Attributes that influence severity: # Class: This attribute classifies the asset according to its operational importance. High-class assets can have a greater impact on the operation if they fail. # 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. Appl. Sci. 2024,14, 10642 18 of 24 # 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 3provides 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%. •Digitalization: #Key for real-time data update. #Digital twins for continuous monitoring and fault simulations. In short, following the methodology proposed by Parra 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 €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. This strategy effectively improves safety and efficiency, providing valuable guidance for replication in other critical infrastructures. During the case study, several significant challenges were faced that impacted the implementation of criticality analysis on the rail network. One of the main challenges was data integration and data quality. The effectiveness of criticality analysis depends on accurate, complete, and up-to-date data. However, the available data were fragmented and came from different sources and systems that were not always integrated, making it difficult to use effectively. Another major challenge was the complexity and heterogeneity of the railway network. The network comprised various interdependent components, each with its technical characteristics, criticality levels, and maintenance requirements. This interdependence required a methodology that could consider the criticality of each element and the system as a whole. An example of the criticality matrix obtained is shown in Figure 4. Appl. Sci. 2024,14, 10642 19 of 24 Appl. Sci. 2024, 14, x FOR PEER REVIEW 19 of 25 critical infrastructures. During the case study, several significant challenges were faced that impacted the implementation of criticality analysis on the rail network. One of the main challenges was data integration and data quality. The effectiveness of criticality analysis depends on accurate, complete, and up-to-date data. However, the available data were fragmented and came from different sources and systems that were not always integrated, making it difficult to use effectively. Another major challenge was the complexity and heterogeneity of the railway network. The network comprised various interdependent components, each with its technical characteristics, criticality levels, and maintenance requirements. This interdependence required a methodology that could consider the criticality of each element and the system as a whole. An example of the criticality matrix obtained is shown in Figure 4. Figure 4. Example of the resulting criticality matrix. In addition, the technological infrastructure needed to implement advanced criticality analysis presented significant challenges. Robust data management systems, specialized software, and advanced analysis tools such as digital twins and machine learning algorithms were required. Procurement, installation, and maintenance of this infrastructure proved costly and technically complex. Finally, training and change management were critical challenges. Adopting new methodologies and technologies involved a significant change in how asset managers and operational staff carried out their work. This change required a considerable commitment to training and organizational change to ensure that staff were technically trained and aligned with the strategic objectives of the data-driven approach. 4.2. Rule Automation In this work, an automated criticality rule evaluation process was developed by implementing a Python 13.10 script. Initially defined in Excel formula format, these rules were translated into nested conditionals in Python for dynamic evaluation on a dataset of maintainable assets. Each asset has different attributes that condition its criticality level in several dimensions, such as failure frequency, safety, environmental impact, and operational cost overruns. The implemented Python code evaluates each criticality rule according to the values of these attributes, allowing complex decision logic to be applied efficiently and consistently throughout the database. The conditional structure of the code replicates the rule-based decisions originally ex-pressed by Excel formulas but with greater flexibility and scalability. The generalized code that performs this evaluation is presented in Appendix A. 5. Discussion During the case study, several significant challenges were faced that impacted the implementation of criticality analysis on the rail network. One of the main challenges was data integration and data quality. The effectiveness of criticality analysis depends on accurate, complete, and up-to-date data. However, the available data were fragmented and came from different sources and systems that were not always integrated, making it Figure 4. Example of the resulting criticality matrix. In addition, the technological infrastructure needed to implement advanced criticality analysis presented significant challenges. Robust data management systems, specialized software, and advanced analysis tools such as digital twins and machine learning algorithms were required. Procurement, installation, and maintenance of this infrastructure proved costly and technically complex. Finally, training and change management were critical challenges. Adopting new methodologies and technologies involved a significant change in how asset managers and operational staff carried out their work. This change required a considerable commitment to training and organizational change to ensure that staff were technically trained and aligned with the strategic objectives of the data-driven approach. 4.2. Rule Automation In this work, an automated criticality rule evaluation process was developed by implementing a Python 13.10 script. Initially defined in Excel formula format, these rules were translated into nested conditionals in Python for dynamic evaluation on a dataset of maintainable assets. Each asset has different attributes that condition its criticality level in several dimensions, such as failure frequency, safety, environmental impact, and operational cost overruns. The implemented Python code evaluates each criticality rule according to the values of these attributes, allowing complex decision logic to be applied efficiently and consistently throughout the database. The conditional structure of the code replicates the rule-based decisions originally ex-pressed by Excel formulas but with greater flexibility and scalability. The generalized code that performs this evaluation is presented in Appendix A. 5. Discussion During the case study, several significant challenges were faced that impacted the implementation of criticality analysis on the rail network. One of the main challenges was data integration and data quality. The effectiveness of criticality analysis depends on accurate, complete, and up-to-date data. However, the available data were fragmented and came from different sources and systems that were not always integrated, making it difficult to use effectively. Another major challenge was the complexity and heterogeneity of the railway network. The network comprised various interdependent components, each with its technical characteristics, criticality levels, and maintenance requirements. This interdependence required a methodology that considered the criticality of each element and the system as a whole. In addition, the technological infrastructure needed to implement advanced criticality analysis presented significant challenges. Robust data management systems, specialized software, and advanced analysis tools such as digital twins and machine learning algorithms were required. Procurement, installation, and maintenance of this infrastructure proved costly and technically complex. Finally, training and change management were critical challenges. Adopting new methodologies and technologies involved a significant change in how asset managers and Appl. Sci. 2024,14, 10642 20 of 24 operational staff carried out their work. This adoption required a considerable commitment to training and organizational change to ensure that staff were technically trained and aligned with the strategic objectives of the data-driven approach. Digitalization significantly improved operational efficiency and decision-making in rail asset management through several key strategies. First, automating data collection and processing allowed for a smoother and more accurate information integration. Sensors installed in the rail infrastructure collected real-time data on asset status, eliminating the need for manual input and reducing the risk of errors. These sensors facilitated a faster and more accurate response to maintenance and operational needs. In addition, the implementation of digital twins provided virtual representations of the physical assets, continuously updated with real-time data. These digital twins allowed asset managers to simulate different operational scenarios and anticipate potential failures before they occurred, resulting in a more proactive approach to maintenance and asset management. The ability to anticipate possible problems improved planning and reduced unplanned downtime, optimizing operational efficiency. Digitalization also promoted interoperability and connectivity between asset management systems, databases, and analysis tools. This connectivity facilitated data integration and provided a more complete and coordinated view of the entire network, improving operational efficiency and decision-making. By sharing and using data effectively, organizations were able to improve cooperation and efficiency in a complex, multinational environment. Furthermore, implementing the data model transformed asset management by providing a powerful tool for continuous criticality assessment, optimizing and preventive maintenance, and improving the safety and reliability of railway operations. The results obtained by implementing data modeling and digitalization in railway asset management have several advantages over traditional methods. Firstly, data-driven methods offer a more accurate and reliable asset criticality assessment due to real-time and historical data, which allow for identifying patterns and trends that may not be evident with more conventional methods. In addition, traditional methods often rely on manual inspections and qualitative assessments, which can lead to decisions based on assumptions or experience. In contrast, the digitalized approach allows for quantitative and objective evaluation, facilitating prioritizing maintenance actions based on concrete data. This prioritization optimizes resource allocation and improves operational efficiency by reducing unplanned downtime by 25%. Digitalization enables greater adaptability and agile response to changes in the network and operating environment, something that traditional methods cannot match due to their rigidity. Simulating failure scenarios and planning proactive interventions are other significant advantages, as they reduce service interruptions and improve availability. Finally, interoperability and data integration in a unified framework facilitates a more complete and coordinated view of the network, something that traditional methods, with their partitioned systems, cannot provide. In short, using data models outperforms conventional methods in accuracy, efficiency, and responsiveness, significantly improving asset management. The study has several limitations that could be addressed in future research to improve the implementation and effectiveness of the data model in railway asset management. For example: • Complexity and heterogeneity of railway networks complicate data integration and application of the criticality model. •Risk of information overload due to the large amount of data available. • Advanced data filtering and analysis techniques are needed to prioritize critical information. •High initial implementation costs for advanced technology and staff training. •Scalability and flexibility of digital solutions require more attention. • Develop scalable models that are adaptable to different operational contexts and infrastructures. • Creation of standardized frameworks to facilitate the integration of digital technologies in various rail networks. Appl. Sci. 2024,14, 10642 21 of 24 In summary, addressing these limitations through future research could significantly improve the effectiveness and efficiency of the data model in rail asset management. For the practical implementation of the digitalization-based approach and the data model for railway asset management, several key recommendations can be made: • Developing a Standardized Framework: It is crucial to develop standardized frameworks and protocols that facilitate the integration of various digital technologies in criticality assessment. This development will help overcome data integration challenges and ensure that systems are interoperable. • Training and Change Management: Adopting new technologies requires significant organizational change. Implementing training programs is essential to ensure that staff are technically trained and aligned with the strategic objectives of the data-driven approach. • Cost–Benefit Analysis: Conducting a detailed cost–benefit analysis is essential to justify investments in digitalization. This analysis will help organizations understand the long-term benefits and cost savings of implementing these technologies. • Optimizing Data Quality: Ensuring data accuracy, completeness, and timeliness is essential for practical criticality analysis. Robust data collection and management processes must be established to minimize discrepancies that can lead to erroneous assessments. • Implementing advanced technologies: Advanced tools such as digital twins and machine learning algorithms can significantly improve criticality analysis. These technologies enable dynamic, real-time analysis, optimizing fault identification and prioritization. • Cybersecurity and Data Privacy: It is critical to implement cybersecurity measures to protect the integrity of information used in criticality analysis, ensuring that the data are secure and used ethically. • Application in Real-Time Environments: We expanded the discussion to address the potential for integrating real-time data sources into our model. Specifically, real-time traffic conditions and geometric measurements from inspection trains could be incorporated to enhance the accuracy and immediacy of the criticality assessment process. By including these real-time data sources, we aim to minimize delays in data-driven decision-making, which, in the current system, can sometimes exceed 12 months. This extended delay can impact the responsiveness of maintenance and criticality evaluation, ultimately affecting the safety and efficiency of railway operations. In addition, new IoT systems currently under development hold significant promise for further enhancing the model’s responsiveness. These IoT systems could enable real-time updates on critical infrastructure components, such as level crossings and electrification systems. Such an advancement would allow the model to process up-tothe-minute information on these essential assets, capturing dynamic changes in asset conditions and usage levels that static or delayed data may not reflect. By integrating these real-time data, the model can provide more accurate and timely insights into the state of railway assets, thereby supporting proactive maintenance decisions and reducing the risk of unexpected failures. These recommendations can facilitate successful implementation and maximize the positive impact of a digital approach to rail asset management. Next steps: The integration of artificial intelligence (AI) in railway maintenance presents considerable advantages for asset management, enhancing the predictive and preventive capabilities essential for operational efficiency and safety. AI can analyze large volumes of real-time and historical data to forecast failures, detect anomalies, and improve maintenance scheduling with a higher degree of accuracy than traditional methods. For instance, machine learning models can identify wear patterns and potential failure points, allowing maintenance teams to preemptively address issues that may otherwise lead to service disruptions. Moreover, AI-powered simulations can model various operational scenarios, providing valuable insights for optimizing maintenance resources. However, Appl. Sci. 2024,14, 10642 22 of 24 implementing AI in this context also poses significant challenges, such as ensuring the availability of high-quality data, developing standardized AI protocols for safety, and overcoming the compatibility issues with legacy systems. Addressing these challenges is essential to fully leverage AI’s potential in improving the resilience and reliability of railway infrastructure but is necessary to develop a strong structure of data and data models to develop really useful applications using AI’s potential. 6. Conclusions This study has demonstrated the transformative impact of digitization and criticality analysis on rail asset management. Integrating a vector of operational attributes, such as failure frequency, unavailability time, asset location, and traffic speed, makes it possible to assess the criticality of assets dynamically in real-time. This data-driven approach allows for the prioritization of critical assets, optimizing resource allocation and improving operational reliability, which is essential for the efficient management of critical infrastructure. The research question focused on whether a systematic method based on digitalization and real-time data could provide more accurate and timely criticality evaluations than traditional static methods. The results confirm that by continuously updating the vector of attributes for each asset, we can more precisely calculate critical parameters, such as failure probability and potential consequences. This method improves maintenance efficiency by focusing on the most critical assets, leading to a 25% reduction in unplanned downtime and a 15% decrease in operating costs. Integrating advanced technologies, such as digital twins and real-time monitoring, further enhances this approach, allowing for continuous updates and re-calculation of criticality as asset conditions evolve. This dynamic assessment model ensures better decision-making, safety, and long-term sustainability of rail operations. In conclusion, developing a practical ontological framework for criticality assessment centered on the vector of operational attributes provides a theoretical and practical guide that can be adapted to various railway contexts. This framework addresses the increasing complexity of rail infrastructure management, offering an effective strategy for the future of asset management in critical environments. Author Contributions: Conceptualization, M.R.H. and A.S.-H.; methodology, A.S.-H.; software, S.S.C.; validation, M.R.H., A.S.-H. and V.G.-P.; formal analysis, A.C.M.; investigation, M.R.H.; resources, V.G.-P.; data curation, A.S.-H. and S.S.C.; writing—original draft preparation, M.R.H.; writing—review and editing, V.G.-P. and A.S.-H.; visualization, V.G.-P.; supervision, A.C.M.; project administration, A.C.M. All authors have read and agreed to the published version of the manuscript. Funding: This work has been developed as part of the AMADIT Project (PID2022-137748OB-C32), funded by MCIN/AEI/10.13039/501100011033/FEDER, EU. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. Conflicts of Interest: The authors declare no conflicts of interest. Appl. 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