The replacement of dysfunctional sensors based on the digital twin method during the cutter suction dredger construction process

In practice, the construction environment of the cutter suction dredger (CSD) is inclement, which results in a high frequency of the key parameter sensor failure. If the key parameter data is missing or false, it will affect the continuity of the construction. This paper proposed a sensor network regression prediction method based on the "Digital Twin" to establish a correlated model between the key sensor and other highly reliable sensors in the CSD construction. The stacking model is trained by learning the CSD construction data that can synchronously calculate any key parameters when the dredger is running. The proposed method is validated on the "Changshi 12" CSD construction case. The results indicate that the method has high prediction accuracy and computes less expensively. Thus, the proposed method could better solve the problem of construction discontinuity caused by key sensor failure.