Abstract
The digital twin (DT) approach has risen in popularity for applications in many industrial process managements. By applying the “Shipyard 4.0” digital transformation trend, the ship industry is developing techniques able to reduce risks by improving operation process management. This study proposes a combination of a DT approach and practical experiment as part of a five-tier framework for DT-driven process management in the ship industry. This study focuses on the characteristic scenarios and crucial parameters within the ship engine system and shipping cargo container in operation procedures. DT-based models and platforms are established in this study based on the basic modeling of Maya and scene rendering of Unity 3D. To address the fusion issue of multi-source heterogeneous data in the ship operation process, a Bayesian neural network (BNN) method is introduced into DT’s virtual model layer and data support layer. By integrating an improved BNN-based algorithm into DT-based models, the collected data can be extracted and aggregated accordingly. In the ship engine room, the operating temperature is selected as a critical parameter, with the best mean percentage deviation (MPD) between DT-driven predictions and test value of 3.18%. During the shipping cargo container process, the results indicate that DT-based models have acceptable performances under different conditions, with optimal MPDs of 5.22%.