Combined Fault Tree Analysis and Bayesian Network for Reliability Assessment of Marine Internal Combustion Engine

Abstract

This paper investigates the reliability of internal marine combustion engines using an integrated approach that combines Fault Tree Analysis (FTA) and Bayesian Networks (BN). FTA provides a structured, top-down method for identifying critical failure modes and their root causes, while BN introduces flexibility in probabilistic reasoning, enabling dynamic updates based on new evidence. This dual methodology overcomes the limitations of static FTA models, offering a comprehensive framework for system reliability analysis. Critical failures, including External Leakage (ELU), Failure to Start (FTS), and Overheating (OHE), were identified as key risks. By incorporating redundancy into high-risk components such as pumps and batteries, the likelihood of these failures was significantly reduced. For instance, redundant pumps reduced the probability of ELU by 31.88%, while additional batteries decreased the occurrence of FTS by 36.45%. The results underscore the practical benefits of combining FTA and BN for enhancing system reliability, particularly in maritime applications where operational safety and efficiency are critical. This research provides valuable insights for maintenance planning and highlights the importance of redundancy in critical systems, especially as the industry transitions toward more autonomous vessels.

Keywords

Fault tree analysis / Bayesian network / Reliability / Redundancy / Internal combustion engine

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