Spare parts management is a critical issue in the industrial field, alongside planning maintenance and logistics activities. For accurate classification in particular, the decision-makers can determine the optimal inventory management strategy. However, problems such as criteria selection, rules explanatory, and learning ability arise when managing thousands of spare parts for modern industry. This paper presents a deep convolutional neural network based on graph (G-DCNN) which will realize multi-criteria classification through image identification based on an explainable hierarchical structure. In the first phase, a hierarchical classification structure is established according to the causal relationship of multiple criteria; in the second phase, nodes are colored according to their criteria level status so that the traditional numerical information can be visible through graph style; in the third phase, the colored structures are transferred into images and processed by structure-modified convolutional neural network, to complete the classification. Finally, the proposed method is applied in a real-world case study to validate its effectiveness, feasibility, and generality. This classification study supplies a good decision support to improve the monitor-focus on critical component and control inventory which will benefit the collaborative maintenance.

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.

The dredger construction environment is harsh, and the mud concentration meter can be damaged from time to time. To ensure that the dredger can continue construction operations when the mud concentration meter is damaged, the development of a dredger with advantages of low price and simple operation that can be used in emergency situations is essential. The characteristic spare mud concentration meter is particularly critical. In this study, a data-driven soft sensor method is proposed that can predict the mud concentration in real time and can mitigate current marine mud concentration meter malfunctions, which affects continuous construction. This sensor can also replace the mud concentration meter when the construction is stable, thereby extending its service life. The method is applied to two actual construction cases, and the results show that the stacking generalization (SG) model has a good prediction effect in the two cases, and its goodness of fit R² values are as high as 0.9774 and 0.9919, indicating that this method can successfully detect the mud concentration.

Deviations between the design and actual shafting occur due to limitations in ship construction accuracy. Consequently, accurately obtaining the relationship between the actual shafting load and displacement relationship based on the design shafting becomes challenging, leading to inaccurate solutions for bearing displacement values and low alignment efficiency. In this research article, to address the issue of incomplete actual shafting data, a transfer learning-based method is proposed for accurate calculation of bearing displacement values. By combining simulated data from the design shafting with measured data generated during the adjustment process of the actual shafting, higher accuracy can be achieved in calculating bearing displacement values. This research utilizes a certain shafting as an example to carry out the application of the bearing displacement value calculation method. The results show that even under the action of shafting deviation, the actual shafting load and displacement relationship model can become more and more accurate with the shafting adjustment process, and the accuracy of bearing displacement values calculation becomes higher and higher. This method contributes to obtaining precise shafting adjustment schemes, thereby enhancing alignment quality and efficiency of ship shafting.

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.