## 2 Literature Background  
RAM analysis is a term that refers to Reliability, Availability, and Maintainability analysis [12]. It is a technique widely used in engineering, especially in the field of system design, to evaluate the parameters in a given system. RAM analysis involves assessing those three factors in order to identify and mitigate potential issues that could impact the performance of a system or a physical asset [13]. As commented above, the use of Monte Carlo methods has expanded and gained acceptance among maintenance practitioners and researchers. Some cases of Monte Carlo experiments applied in maintenance and reliability analysis can be found in [14; 15; 16]. Several RAM models can be used during the design phase to optimize or modify system configuration, or during the operational phase to identify areas for improvement or assign resources to preventive maintenance or take other actions, such as decommissioning or dismantling part of or an entire system. RAM analysis usually involves the following steps [17]: identifying critical components, establishing failure modes and consequences, the assessment of reliability of each component, and the overall reliability of the plant. Once these steps have been conducted through mathematical models and simulations, the optimization may take place. Therefore, results from those stages can be used to optimize the design and maintenance strategies of plants, minimizing downtime and improving overall reliability. Most of the time, RAM analysis is preceded by an FMECA-type analysis; that is, the main failure modes and their effects are mapped and analyzed [18]. Based on this identification, the levels of criticality and the corresponding priorities are defined. Frequently, this analysis is performed using the failure records of the system. However, in many cases, this analysis is based on subjective values or simple estimates of mean time between failures (MTBF) and mean time to repair (MTTR) using approximations or ranges of values. Accurately determining the probability of failures is often challenging, as many evaluations are based on non-existent or incomplete information, or are expressed in qualitative terms such as \"probable\" or \"very high\", among others [19]. Additionally, components and systems typically degrade over time, introducing multiple states that are difficult to objectively assess using traditional FMEA. Furthermore, conventional FMEA heavily relies on the diversity and expertise of the team of specialists, who may not always be available. Moreover, the sharing of experiences among various industrial practitioners is complex, thereby limiting the broader application of FMEA. In the last decade, numerous efforts have been made to enhance the performance of FMEA. Approaches have been proposed to express interdependencies between causes and effects, and a method for causal reasoning using the FMEA language has been developed. Recently, techniques employing membership functions to describe the characteristics of a component or asset in fuzzy terms have been introduced. Additionally, methods based on max-min inference and defuzzification have been applied in criticality analysis. Other techniques rely on probability distribution theories to address uncertainties and the existence of multiple failure modes. The gas industry is no exception to these considerations. Criticalities are high, and many installations operate under demanding conditions and with very high utilization levels. The study by [PERSON] et al. [5] highlights the need for further research on the reliability and value of gas storage to enhance energy security. [PERSON], [PERSON], et al. [20] analyze the role of operational factors in an LNG plant, such as plant design and equipment performance, in determining the effectiveness of maintenance activities.