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This paper presents a comprehensive methodology for reliability assessment of marine systems which integrates Failure Modes, Effects, and Criticality Analysis (FMECA) and Bayesian Networks (BNs). The proposed FMECA-BN framework enables a structured and probabilistic approach to modelling complex failure interdependencies, particularly suited for systems with limited failure data and high operational criticality. The methodology is applied to a case study of a shipboard compressor, as one of the key components in marine systems. The analysis begins with system definition and failure identification, where maintainable items and associated failure mechanisms are determined. These elements are mapped to BN components - nodes, states, and conditional dependencies - enabling probabilistic inference of system reliability. Critical failure modes are identified and modelled within the BN to calculate the probability of critical compressor failure. To evaluate reliability improvement strategies, eight scenarios were analysed: four involving advanced sensor-based detection for prevention of failure mechanisms, and four introducing redundancies in maintainable items. The results indicate that simulated improvements can reduce the probability of critical failure by up to 18.08 %. Additionally, Risk Priority Numbers (RPN) were calculated for each scenario, supporting informed decision-making with respect to maintenance strategy selection. The integration of FMECA with BN offers a powerful tool for dynamic reliability analysis and maintenance planning in marine systems, where conventional deterministic methods may fall short. This hybrid approach facilitates a deeper understanding of failure behaviour, enhances predictive capability, and supports proactive reliability management.
Jovanović et al. (Tue,) studied this question.