## 1 Introduction  
As the global community pursues total decarbonization, the prominence of natural gas grows, owing to its minimal carbon emissions and vast reserves. However, such supply must be supported by industrial facilities and physical assets with high levels of reliability and availability. Given the relevance of operational continuity in LNG plants, several papers reveal the need to keep reliability parameters under control. According to the work of [PERSON] et al. [1], rigorous, adequate, and integrated maintenance approaches are essential to keep equipment running smoothly. Reliability indicates the probability that an asset or system will operate without failures, fulfilling its intended purpose regarding a given level of confidence. According to [PERSON] [2], reliability is considered as the best measure of the integrity of a system. He further notes that, \"No industry in any country can progress effectively without the knowledge and implementation of reliability engineering\" [2]. Therefore, reliability analysis is considered a critical aspect in the design and operation of Natural Gas Liquefaction (LNG) plants, and it plays a crucial role in ensuring safety and the efficient operation of these types of facilities [3]. It helps to evaluate the ability of plant assets and systems to perform their intended functions and the likelihood of failures or unplanned shutdowns. In addition, reliability analysis can be used to improve design and optimize maintenance strategies. As [PERSON] (2019) states, \"RAM analysis is the basis for complex system performance analysis. To demonstrate such a methodology, the RAM analysis steps, such as scope definition, lifetime data analysis, modeling, simulation, critical analysis, sensitivity analysis, and conclusions, will be discussed\" [4]. The proposed approach allows for the implementation of RAM analysis based on incomplete information and fuzzy modeling, which we believe adds value to our proposal. It is important to note that reliability analysis is an ongoing process that needs to be repeated regularly to keep up with changes in plant design and operating conditions, ensuring that the plant continues to operate at maximum efficiency and reliability. One of the most widely used tools to ensure reliability in LNG plants is Reliability Centered Maintenance (RCM), through the identification of critical components and their failure modes and critical effects [5]. RAM analysis is a term that refers to Reliability, Availability and Maintainability analysis [6; 7]. 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 [8]. As systems become increasingly complex, it is more difficult to compute or estimate reliability, maintainability, and availability parameters in an analytical manner [9]. As computers have advanced in processing speed and memory capacity, 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 [10; 11]. However, various steps in the application of RAM analysis are based on semi-quantitative information, incomplete data, or estimates derived from manuals. To address the challenges associated with semi-quantitative information, incomplete data, or estimates derived from expert judgements, this work is intended to propose a novel procedure based on a Fuzzy-FMEA integrated into a RAM analysis framework to analyze and optimize the structure, operation, and maintenance of an engineered system. In addition, the proposed framework aims to address uncertainty by incorporating Monte Carlo simulation experiments. Subsequent sections of this paper are in Section 2; the literature background is discussed. Next, in Section 3, the proposed methodology is described.