Reliability Assessment of Ship Lubricating Oil Systems Through Improved Dynamic Bayesian Networks and Multi-Source Data Fusion

The operational efficiency and reliability of the ship’s lubrication oil system directly impact the vessel’s safety. Traditional reliability analysis methods struggle to effectively handle the system’s dynamic characteristics and multi-source data analysis. To address these issues, this study proposes an innovative method that integrates feature dimensionality reduction, a dynamic Bayesian network of gravity model to improve the accuracy of system reliability analysis. First, the proportional hazards model is used to evaluate the operational reliability of each component, providing a quantitative basis for assessing the system’s health status through failure rate estimation. Then, a dynamic Bayesian network model is employed for overall system reliability analysis, fully considering the impact of multi-state devices and different maintenance strategies. The proposed DBN-based reliability assessment method achieves significant improvements over the traditional Fault Tree Analysis (FTA). The reliability of the main lubrication oil system (GUB) increases from 0.169 to 0.261, representing a 9.2% improvement; under scheduled maintenance conditions, the system reliability stabilizes at approximately 0.9873 after $0.4\times {10}^5$ h, compared to only 0.24 without maintenance. The proposed method effectively evaluates the reliability of the lubrication oil system, and the maintenance strategy using this method can greatly improve the reliability, providing strong support for scientifically guiding maintenance decisions.