Digital twin-driven virtual sensor approach for safe construction operations of trailing suction hopper dredger

Abstract The stable and safe operation of Trailing suction hopper dredger (TSHD) is one of the most crucial considerations for ensuring its high dredging productivity. However, the instability and sudden failure of physical sensors pose challenges to the monitoring of dredging process. To address these issues, we propose a structure of digital twin-driven virtual sensor (DTDVS) for the construction safety of TSHD. Considering the potential internal relations among construction data, we compare the performance of four machine learning algorithms in predicting the torsional vibration in mechanical failure. The results showed that these algorithms provide high prediction accuracy (R2 > 0.9). Then the DBN model with the best performance was selected as a part of the virtual sensors to predict and analyze the status of TSHD. The digital twin technology provides a more stable and environmentally friendly scheme for TSHD construction safety control. On the one hand, the DTDVS assists physical sensors to monitor the construction state, overcoming the limitation of the sensors on detection targets which are difficult or costly to measure directly. On the other hand, by analyzing the residual between the physical sensor and the virtual sensor, the construction behavior can be diagnosed, and the fault situation can be pre-warned accurately. This improves the time utilization of TSHD and provides an important guarantee for the construction safety.