Productivity regression analysis of cutter suction dredger considering operating characteristics and equipment status

Abstract

In order to optimize the operation parameters of cutter suction dredger in real time and adjust productivity as needed, a construction optimization strategy based on real-time productivity regression analysis is proposed. Machine learning methods, including Support Vector Regression (SVR), Gradient Boosting Regression Tree (GBRT), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM) and a Super Learner that made up of them, are used to mine relevant features based on the big data of operation characteristics and equipment status. Firstly, the working principle of cutter suction dredger is analyzed, the features that need real-time monitoring are determined, and the above features are classified. Then, some missing values and outliers in the data are deleted. Next, Lasso method is used to eliminate the variables that are not related to the regression target, and the redundant variables are combined. In addition, five machine learning methods are used to train and test the off-line productivity data of cutter suction dredger. And they are used to fit recent online productivity data. Super Learner performed best, which achieved the highest R2 (0.917), the lowest RMSE (75.096) and MAE (61.422) in the five models for online regression. Furthermore, the calculation time of each model is discussed, and the feasibility of the method proposed in this study for real-time regression of online productivity data has been confirmed. Finally, the importance of characteristics is analyzed to provide guidance for dredging operations under restricted construction conditions. According to the regression results and the importance of features, operators can give priority to adjusting some features to adjust the real-time construction productivity of dredger.