# Digitalization and Dynamic Criticality Analysis for Railway Asset Management
## 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 \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Features** & **Water Network** & **Gas Network** & **Electticity Grid** & **Teleco Network** \\ \hline
**Main Asset Reference** & Piping and valves & Pipelines and & Transformers and & Routers and switches \\
**Expression of Risk in** & Impact on drinking & Impact on gas & Impact on the stability & Impact on service quality \\
**Terms of Value** & water supply & distribution and safety & of electricity supply & and reputation \\
**Semi-quantitative** & Probability of failures and & & Probability of failures at & Probability of node \\
**method** & Consequences in terms of & Likelihood of leakage and & critical nodes and & failures and consequences \\  & service interruption & & consequences in terms & in terms of loss \\  & Reliability and & Security and & of outages & of connectivity \\
**Business Value Factors** & availability of water & continuity of gas & Reliability and energy & Quality of service and SLA \\  & supply & supply & efficiency & compliance \\
**Probability and** & Historical failure & Incident analysis and & Failure models and & Historical failure analysis \\
**Consequences of Failure** & analysis and impact & impact simulations & cascade simulations & and impact simulations \\
**Severity Distributions** & Failure histograms & Incident histograms & Fault distributions & Failure histograms and criticality analysis \\  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary Table of Characteristics and Data Used in Criticality Analysis by Network Type. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Key Element** & **Railway Networks** & **Utility Networks** \\
**for Criticality Analysis** & & **(Water, Gas, Electricity, Telecommunications)** \\ \hline
**Critical Components** & Tracks, signaling systems, trains, control units & Pipelines, valves, pipelines, compressor stations, transformers \\
**Criticality Criteria** & Operational security, continuity of service, & Security of supply, resilience, quality of service \\
**Methodologies** & Failure mode and effects analysis (FMEA), Fault & Risk-based corrective action (RPCA), FMEA, simulations based on historical data analysis \\
**Digital Technologies** & Real-time monitoring, failure prediction with & Predictive analytics, maintenance automation \\  & digital twins & Loss of supply, impact on quality of service, economic \\
**Impact of Failures** & Service interruptions, delays, security impact & Loss of supply, impact on quality of service, economic \\
**Optimization Objectives** & Minimization of failures, optimization of & Improved resilience, reduced operating costs, customer \\  & maintenance & satisfaction \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison between rail and utility network key element for criticality analysis. practices. The previous research has explored how digital technologies can transform and optimize railway maintenance. * [PERSON] [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.