## 3 Methodology  
A comprehensive fault diagnosis methodology involves several steps to systematically detect, isolate and understand faults in a system. The proposed approach involves system understanding, data acquisition and preprocessing, feature selection and extraction, fault detection, fault localization and isolation, fault classification and severity assessment, diagnostic decision-making, feedback and continuous improvement. Understanding the system architecture and expected behaviors is fundamental to fault diagnosis methodologies, ensuring a comprehensive grasp of critical components and operational norms. Acquiring and preprocessing relevant data involves collecting and refining information from sensors and monitoring devices and refining it through filtering and normalization to enhance accuracy. The selection of informative features is crucial for capturing meaningful patterns in the data, which facilitates effective fault detection and localization. Applying fault diagnosis algorithms enables the identification of anomalies or deviations from normal system behavior, laying the foundation for subsequent diagnostic steps. Localizing and isolating faults involves narrowing down potential causes to specific components or subsystems, thereby aiding in the identification of root causes and effective mitigation strategies. Classifying and assessing fault severity helps prioritize response actions, ensuring that critical issues are addressed promptly to minimize disruptions and optimize system performance. Decision support systems play a pivotal role in guiding informed fault response decisions considering various factors such as safety, reliability and cost implications. Continuous feedback and improvement drive the evolution of fault diagnosis methodologies, ensuring their effectiveness in terms of addressing emerging insights and adapting to changes in system behavior. To properly analyze a problem, it is necessary to utilize three primary data sources: operational parameters, maintenance history and the technical condition of components, to allow for the estimation of future values based on trends, seasonal variations and cyclical phenomena, in accordance with the theoretical principles of ML in the product-program lifecycle [45; 46]. Fault detection and diagnosis methods, such as FTA, FMEA and ETA, are integral components of PRA methodologies. FTA, a systematic approach to identifying and analyzing potential system failures, is often utilized within PRA to assess the likelihood and consequences of various fault scenarios. Similarly, FMEA and ETA complement PRA by providing detailed evaluations of the failure modes and their effects, as well as the sequences of events following fault occurrence, respectively. In research [47], authors identified limitations in the traditional risk assessment method and proposed an improved analysis model using frequency-impact values plotted on a four-quadrant evaluation chart. Meanwhile, research [48] evaluated the failure modes using the FMECA method and prioritized the risk factors, which were then used for opportunity analysis regarding the implementation of Condition Monitoring Systems (CMS) and CBM practices. Potential failures in FTA-based naval propulsion systems are outlined in research [49]. The impact of disruptive events on critical ship components is assessed in research [50], which proposed a method for evaluating operational time without human intervention. Research [51] explored FTA's applicability across industries, including maritime ones and incorporated fuzzy logic to address event interdependencies. Also, research [52] analyzed failures in the propulsion systems of four identical ships, identifying key components for reliability calculations and suggesting improvements through Reliability Availability and Maintainability (RAM) analysis and FTA.