## 4 Results II--Review of Research Clusters
### Cluster 1: Risk/Accident Analysis
#### 4.2.2 Sub-Cluster 2: Risk Analysis
Additionally, continuous monitoring and updates are essential from a risk management perspective to ensure its long-term effectiveness. [PERSON] et al. (2023) [45] developed a Risk Awareness (RAW) tool designed to systematically monitor and prevent potential hazards encountered in the harsh environments of small fishing vessels (SFVs). The primary objective was to identify accident risk factors faced by crew members during vessel operations and provide situational awareness (SA) information that enables proactive responses, thereby reducing the likelihood of accidents. To achieve this, a risk analysis technique based on the BN model was applied to categorize risk factors into technical, environmental, and human-related elements, and to quantitatively assess the impact of each factor on accident occurrence. Additionally, to address the subjectivity and inconsistency that may arise during the construction of Conditional Probability Tables (CPTs) in BN, the study introduced predefined probability scales to minimize expert opinion variability and enhance the model's reliability. The results demonstrated that the proposed RAW tool enables crew members to track potential hazards in real-time and implement appropriate preventive measures based on SA information. However, BN-based models are complex to build and require further examination to ensure their applicability across diverse vessel types and operating environments. Thus, the study suggests simplifying the model construction process to improve its practicality and robustness. Meanwhile, among the causes of risk, mechanical failures can cause major accidents. Therefore, reliability assessment of machines can be considered a type of safety assessment. In industries where safety and reliability are important, Failure Mode, Effects, and Criticality Analysis (FMECA) is a commonly used method to identify potential system failures in advance and implement preventive measures. FMECA is a systematic risk analysis technique that identifies potential failure modes, assesses the impact of each failure, and prioritizes them accordingly. Through this process, the primary goal is to prioritize critical failure modes and enhance the system's reliability and safety. [PERSON] et al. (2017) [14] proposed a failure analysis based on the DST to address the uncertainties and subjective judgments in the conventional FMECA approach. In the traditional FMECA method, the Risk Priority Number (RPN) does not adequately handle uncertain expert opinions and faces issues with the equal weighting of the parameters: Occurrence, Severity, and Detection. To overcome these limitations, the study utilized DST to convert ambiguous or interval-valued expert opinions into Belief and Plausibility distributions, thus deriving the priority of risk factors. This method was applied to the propulsion system of fishing vessels in the Sicilian region, identifying a total of 28 failure modes and analyzing the risk levels of each. The results showed that the most critical failure mode was the pumping failure of the gear-inverter oil pump, and the DST-based FMECA approach could more effectively account for uncertainties compared to the traditional RPN method. While this approach enables more reliable analysis, it can lead to a rapid increase in computational complexity in large-scale systems, resulting in constraints on time and resources. Another study proposed a method to evaluate the reliability of mechanical devices. [PERSON] et al. (2022) [36] aimed to develop a Risk-Based Maintenance (RBM) strategy for the Main Propulsion System (MPS) of fishing vessels. To address the issue that traditional preventive and condition-based maintenance approaches are not suitable due to high costs and operational disruptions, the study proposed a model that combines the Goal-Directed Risk Identification Technique (Goal-DRIT) with an Object-Oriented Bayesian Network (OOBN) to identify critical failure components of the MPS and optimize the maintenance intervals.