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.

耙吸式挖泥船（TSHD）的稳定和安全运行是确保其高挖泥生产率的最重要考虑因素之一。然而，物理传感器的不稳定和突然故障给疏浚过程的监测带来了挑战。为了解决这些问题，我们为TSHD的施工安全提出了一种数字双驱动虚拟传感器（DTDVS）结构。考虑到施工数据之间潜在的内部关系，我们比较了四种机器学习算法在预测机械故障中的扭转振动方面的性能。结果表明，这些算法提供了较高的预测精度（R ² > 0.9）。然后选择性能最好的DBN模型作为虚拟传感器的一部分来预测和分析TSHD的状态。数字孪生技术为TSHD施工安全控制提供了更稳定、更环保的方案。一方面，DTDVS辅助物理传感器监测施工状态，克服传感器对检测目标的限制，直接测量困难或成本高。另一方面，通过分析物理传感器和虚拟传感器之间的残差，可以诊断施工行为，准确预警故障情况。这提高了TSHD的时间利用率，为施工安全提供了重要保障。