Intelligent Maintenance of Ship Lubricating Oil System by Integrating Dynamic Bayesian Network and Deep Learning Model

The ship lubrication system is susceptible to multiple influencing factors—including temperature, pressure, oil quantity, and oil quality—under complex operating conditions, leading to nonlinear fault evolution patterns that make traditional monitoring methods inadequate for timely and accurate early warning. To address these challenges, this study proposes an intelligent maintenance model integrating an improved Dynamic Bayesian Network (DBN) with a Convolutional Neural Network-based Bidirectional Gate Recurrent Unit (CNN-BiGRU). The approach enhances system evolution modeling capabilities through optimized dynamic state transition mechanisms, utilizes CNN-BiGRU to extract multivariate time-series features, and combines probabilistic inference for abnormality detection and risk assessment. Experimental results on the Open Ship Machinery Data Set (SMD) and Oil Analysis Data Set (OAD) demonstrate that the proposed model achieves an F1-score of 95.52% and an AUC of 0.971, outperforming multiple competing models. Furthermore, the model exhibits superior early warning performance and stability under various abnormal operating conditions and load fluctuations. These findings confirm that the proposed methodology significantly improves fault monitoring accuracy and intelligent maintenance efficiency for ship lubrication systems, providing robust technical support for reliable equipment operation under complex environments