An integrated grey-box model for accurate ship engine performance prediction under varying speed and environmental conditions

The development of ship engine fault diagnosis has led to an increased interest in predicting marine engine performance under various environmental and operating conditions. However, predicting engine performance using a single model is limited due to the various characteristics of ship engines depending on speed and environmental conditions. To address this issue, we propose a grey box model (GBM)-based ship engine performance prediction framework that combines the white box model and black box model (BBM) appropriately for both low- and high-speed operating conditions. The application of data preprocessing techniques, such as clustering and cleaning, under specific engine and environmental conditions, combined with dimensionality reduction techniques (partial least square and principal component) and the use of WBM/BBM models under classified speed conditions, makes the proposed framework accessible to real-world operators through data-driven approaches and domain knowledge. We demonstrate that the proposed framework improves the robustness, accuracy, and efficiency of engine performance predictions by considering the characteristics of each speed in real-world navigation data, compared to using single models.