The installation quality of a propulsion shaft system directly affects the lubrication statuses of the bearings. The quality of the shaft system installation not only affects the progress of ship construction, but also the safety, stability, and reliability of the shaft system. This article takes sliding bearings in ship shafting as the research object and establishes a hydrodynamic lubrication model of sliding bearings while considering installation errors to address the issue of installation errors of ship stern bearings. The finite difference method and super-relaxation iteration method are used to solve the problem, and the influences of bearing installation errors on bearing lubrication characteristic parameters are explored. An installation error of the stern bearing can lead to an increase in the film pressure at both ends of the stern bearing in the axial direction, leading to a decrease in the lubrication status of the bearing. Poor lubrication and wear faults of the stern bearing are prone to occur at both ends of the stern bearing. As the installation error of the stern bearing increases, the minimum film thickness of the stern bearing decreases and the maximum film pressure increases, and as the installation error increases, the sensitivity of the aft stern bearing to the vertical installation error is greater than that of the lateral installation error, and the sensitivity of the fore stern bearing to the lateral installation error is greater than that of the vertical installation error. The sensitivity of the lateral and vertical film forces at both ends of the aft stern bearing and the fore stern bearing is greater than that of the middle part; the installation error of bearings has a significant impact on the lubrication characteristics of bearings.

Dredging is a basic construction for waterway improvement, harbor basin maintenance, land reclamation, environmental protection dredging, and deep-sea mining. The dredging process of cutter suction dredgers is so complex that the operational data show strong characteristics of dynamic, nonlinearity, and time delay, which make it difficult to predict the productivity accurately via basic principles models. In this paper, we propose a novel integrating PCA-LSTM model to improve the productivity prediction of cutter suction dredger. Firstly, multiple variables are reduced in dimension and selected by PCA method based on the working mechanism of cutter suction dredger. Then the productivity is predicted via mud concentration in long short-term memory network with relevant operational time-series data. Finally, the proposed method is successfully applied to an actual case study in China. Also, it performs well in the cross-validation and comparative study for several important characteristics: (i) it involves the operational parameters based on the mechanism analysis; and (ii) it is a deep-learning-based approach that can deal with operation series data with a special memory mechanism. This study provides a heuristic idea for integrating the data-driven method and supervision of human knowledge for application in practical engineering.

During the operation and service of a ship, its power system will affect the stability, reliability, and safety of the ship’s power system and the ship’s vitality if there are typical problems, such as unstable operation and vibration of the shaft system. If the tail bearing is not properly installed, it will lead to increased vibration at its support during operation, which will cause the propulsion system components to come loose and even produce destructive accidents. This paper combines the theory of multi-degree-of-freedom system dynamics to study the propulsion system vibration modeling technology based on the bearing–mounting error, analyze the mapping law between the bearing–mounting error and the shaft system vibration, construct a shaft system vibration model with the bearing–mounting error included, and analyze the influence of the bearing vertical mounting error and lateral mounting error on the vibration performance of the shaft system. This paper establishes the equations of motion of the shaft system with bearing–mounting errors and analyzes the relationship between the bearing vertical mounting errors and lateral mounting errors and the amplitude, speed, and acceleration of the paddle shaft system. The analyzed results show that the vibration response of the shaft system gradually increases with the increase in the bearing–mounting error. With the increase in the bearing vertical mounting error, the increase in vibration amplitude and the transient response of vibration acceleration in the vertical direction is larger than that in the horizontal direction, and the sensitivity of the transient response of vibration acceleration in the vertical direction to the bearing vertical mounting error is larger than that in the horizontal direction. With the increase in the bearing lateral mounting error, the increase in the vibration acceleration transient response value of the paddle shaft system in the horizontal direction is larger than that in the vertical direction, and the sensitivity of the vibration amplitude and vibration acceleration transient response to the bearing lateral mounting error in the horizontal direction is larger than that in the vertical direction. The bearing vertical installation error has a greater effect on the vibration of the paddle shaft system in the vertical direction than in the horizontal direction, and the bearing lateral installation error has a greater effect on the vibration of the paddle shaft system in the horizontal direction than in the vertical direction. The results of this paper can provide a theoretical basis and technical reference for the installation and calibration of ship propulsion system.

The suction-lifting system of cutter suction dredgers consumes a large amount of energy. Optimizing their performance is of great significance for enhancing the overall efficiency of dredgers. This study proposes the effective specific cutting energy, a new indicator suitable for evaluating the energy consumption of the cutting system of cutter suction dredgers. It reflects the cooperation state between the cutter system and the pump-pipe system and has important reference value for improving construction efficiency. The calculation method of the effective specific cutting energy is given, which is calculated by the cutter motor power, slurry concentration, and slurry flow rate. Based on the machine learning framework, a model framework for predicting the specific cutting energy according to the relevant parameters of the suction-lifting system is constructed. Real ship data from the cutter suction dredger “Changshi 12” are used for experiments. First, eigenvalue screening is carried out based on the dredging knowledge and mechanism, then outliers are removed, and finally data processing is performed using Spearman correlation coefficient and PCA dimensionality reduction techniques. Subsequently, five machine learning algorithms, such as RF and XGBoost, are used in combination with a grid search to find the optimal hyperparameters, and Lasso is used as the meta-learner to integrate the prediction results. The experimental results show that the Random Forest and Stacking models have high prediction accuracy for slurry concentration, cutter motor power, and slurry flow rate, verifying the feasibility of this method.

Local installation errors in stern-tube bearings can substantially affect shaft alignment, yet conventional calculations often represent these errors only as nominal support displacements. This study extends the three-moment-equation framework by explicitly parameterising vertical and lateral offsets of the aft stern bearing within the compatibility equations for a multi-supported shaft beam. The method is benchmarked against published bearing-elevation data. Relative to the reference calculation, the mean absolute error (MAE) and root-mean-square error (RMSE) are 0.043 and 0.055 mm, respectively; relative to the reported installation elevations, the corresponding values are 0.229 and 0.281 mm. The method is then applied to the shafting system of a large container ship to evaluate changes in shaft deformation and slope, bearing clearance, maximum pressure, bearing load, shear force, bending moment, and support stiffness. For this case, the vertical offset is the dominant disturbance: an increase from 0 to 0.1 mm raises the maximum aft-bearing pressure by 12.19% and reduces its support stiffness by 14.39%. The effects of lateral offset remain limited up to 0.3 mm, whereas a secondary contact region develops at approximately 0.4 mm. This transition is case-specific and should not be interpreted as a universal tolerance limit. The method is intended as a rapid preventive tool for assessing installation tolerances; however, its pressure and stiffness predictions require further validation of the contact model and additional experimental evidence.