## 3 Proposed Methodology  
This proposed work was developed using two primary data sources. First, historical data was utilized to approximate failure frequency and probability distributions. Second, severity and detection criteria were assessed by industry experts. In certain instances, the OREDA handbook served as a foundational reference. By integrating these diverse data sources with the effectiveness of a fuzzy approach, the study easily addresses uncertainty and imprecision in failure and repair data, delivering a more robust and reliable evaluation [34]. Regarding the choice of triangular fuzzy numbers, this was motivated by their ability to efficiently handle imprecision and uncertainty in system parameters [35]. Triangular fuzzy numbers were selected for their simplicity in modeling vague or incomplete data, making them particularly useful in environments where exact failure and repair data are not always available. Although other fuzzy number types exist, such as trapezoidal or Gaussian fuzzy numbers, triangular fuzzy numbers offer a practical balance between computational simplicity and adequate representation of uncertainty [36]. A procedure is proposed that employs fuzzy terms to complement an FMECA analysis, thereby facilitating the use of imprecise or incomplete information regarding the reliability and maintainability values of the identified failure modes. The methodology proposed in this paper is illustrated in the diagram shown in Figure 1. This figure will provide the reader with a comprehensive understanding of the proposed methodology and the sequence of analysis. Additionally, the following paragraphs elaborate on the elements that constitute the methodological development, which contribute to the objectives outlined for this study. The diagram depicted in Figure 1 shows the methodological proposal. At the first stage, the operation and system's taxonomy are described. The information is obtained for each piece of equipment, and their corresponding drawings and additional technical information regarding the assets are compiled. With this, a descriptive scheme of the  
Figure 1: Diagram explaining the methodology applied. stages of the operational process is made. Once the functional and topological relationships between the system components have been identified and mapped, the Reliability Block Diagram (RBD) is drawn up, where each of the subsystems is established according to its given logical configuration (series, parallel, redundancy, etc.) [37]. An RBD constitutes a graphical representation for assessing the reliability of a system [38]. It shows the logical connections of the components involved in the system which are necessary for the success of the system. Since it represents the logical relationships necessary for the operation of the system, an RBD does not necessarily represent how the elements are physically connected in the system, although this is considered as far as possible (European Committee for Electrotechnical Standardization, 2006). In addition, such diagrams incorporate the behavior of the reliability and maintainability of each piece of equipment through the values of the Mean Time Between Failures (MTBF) and Mean Time to Repair (MTTR). This constitutes the basis for the quantification of the influence of each piece of equipment and subsystem on the reliability and availability at a systemic level thanks to the so-called RAM (Reliability, Availability, and Maintainability) analysis [39]. Subsequently, the use of a fuzzy logic-based FMEA approach is proposed [40,41], which extends the criticality analysis method to FMEA and constructs a fuzzy evaluation system to generate inputs for the RAM analysis experiments. This methodological stage will be analyzed in detail in the following section. Fuzzy logic allows for handling imprecise data and does not assume complete independence of the combined evidence or ideas, facilitating the qualitative evaluation of the relationships between failure modes and their effects.