## 2 Literature Review  
Fault detection and diagnosis (FDD) ensures system reliability and safety by identifying, isolating and addressing faults using techniques like Fault Tree Analysis (FTA) and Failure Mode Effect Analysis (FMEA), while recent advances in AI and ML enhance predictive maintenance and optimize fault management in complex systems. FDD involves identifying, isolating and understanding system faults to ensure the reliability, safety and efficiency of complex systems across various domains. The first step is detecting abnormalities using sensors, monitoring devices, data analysis techniques and expert knowledge. Once detected, the root cause is isolated by analyzing system behavior and sensor data through fault isolation techniques such as FTA, root cause analysis and model-based reasoning. Faults are then classified based on type, severity, duration and impact on performance. Diagnosis determines the fault's nature and effects, while prognosis predicts the system's future behavior considering fault progression. Fault diagnosis systems offer decision support tools, helping operators and engineers respond effectively to faults. Continuous system monitoring refines fault detection algorithms, improving fault isolation techniques and system reliability. Fault diagnosis (FD) varies by system type and fault nature, but it is crucial for timely detection and resolution to prevent downtime and catastrophic failures and optimize system performance. Safety and reliability are critical in technical system design and operation, whether involving machines, vehicles, or complex installations. Probabilistic risk assessment (PRA) processes, including FTA, FMEA and Event Tree Analysis (ETA), are widely used to manage these risks. These methods depend on component failure data for quantitative analysis, though data collection is often incomplete due to monitoring limitations in maritime systems [6]. FTA, developed by Bell Telephone Laboratories in 1962 and later advanced by Boeing, analyzes potential failure causes and provides a logical, graphical model of system failures. It is a structured method for qualitative and quantitative failure analysis [7; 8] but requires expert involvement and focuses only on potential failures [9; 10; 11]. FMEA, first used in the aerospace industry, identifies and prioritizes potential failure modes by assessing severity, occurrence and detectability, aiming to mitigate risks and improve system reliability [12]. When paired with Criticality Analysis (CA), FMEA becomes FMECA, which quantitatively prioritizes system vulnerabilities. Both FTA and FMEA are recommended for reliability applications, with research suggesting forward and backward integration of the methods for improved risk evaluation [13]. Recent studies have focused on enhancing maritime system reliability and risk assessment through the integration of FTA, FMEA and other techniques [14; 15; 16; 17; 18]. Research [19; 20; 21; 22] focused on assessing risks and improving reliability in maritime systems. For example, some researchers have proposed reconfigurations to increase system reliability, others have used reliability block diagrams to modify ship systems, and some have applied advanced techniques like the Fuzzy Multi-Criteria Decision-Making Approach [21] (FMCDMA) and Dynamic Fault Tree Analysis (DFTA) to prioritize maintenance actions [22]. The Risk Priority Number (RPN) method (1) in FMEA prioritizes failure modes based on severity (S), occurrence (O) and detectability (D), though its simple multiplication formula has been widely critiqued. \[\text{RPN}=\text{S}\times\text{O}\times\text{D}, \tag{1}\]  
Alternative approaches [23; 24; 25; 26], such as weighted RPN calculations and fuzzy logic, have been proposed to improve accuracy. Moreover, the integration of AI and ML in predictive maintenance has gained significant attention, with AI techniques used to predict equipment failures and optimize maintenance schedules [27; 28; 29].