Cutting state estimation and time series prediction using deep learning for Cutter Suction Dredger

Abstract

With the increasing environmental protection standard, the Cutter Suction Dredger (CSD) construction is required to guarantee precise dredging in some special areas. Specifically, dredging the side slope of channeling, which requires the operator to accurately control the CSD. However, the underwater cutting environment of CSD construction is complicated and the soil kinds are sometimes spatially different, which makes it extremely difficult for the operator to accurately control the CSD. Therefore, we propose a state-of-the-art time series prediction model based on CNN-LSTM Encode-Decode, which can predict the cutting status of the cutter in advance and also ahead estimate the soil kinds to be cut. First, we establish the relationship between the cutting torque and the cutter motor power (CMP) and rotation speed (CMRS). By ahead forecasting the CMP and CMRS, the cutting torque at the next moment can be obtained. Then, the experienced operator can estimate the kind of underwater cutting soil by torque. In addition, we also compare the ahead one-step forecasting and ahead multi-step forecasting of this method with other models and conduct an experiment to verify the performance. The comparison results demonstrate that our approach significantly outperforms, which can help the operator's ahead perception of the underwater cutter status and ensure precise dredging.