Deep learning is increasingly prevalent in the bearing fault diagnosis, while the deficiency of fault samples could diminish the diagnostic efficacy of data-driven models that depend on extensive training data. For that, a novel multiscale physics-informed network (MPINet) is proposed for bearing fault diagnosis with small samples. Our fundamental idea is incorporating physical knowledge into the training process for enabling the model could better learn the fault features. To pursue this goal, a physics-informed block (PIB) is developed to extract fault features, which is customized for each failure mode. By this process, multiple independently trained PIBs encode the physical knowledge of their corresponding failure mode into the model, and thus yield multiscale fault features. Finally, the diagnosis result is obtained by using a new classifier head to merge these multiscale features. Extensive experimental results show that our MPINet can obtain superior diagnosis performance with small samples.

Shipbuilding is a complex systematic project involving various processes. In this study, a function-oriented quality control method is proposed to control shipbuilding quality effectively. The method decomposes the ship construction process from the functional perspective to study the relationship between the intermediate products and the functions, afterwards analyse the impact mechanism of the construction process on the function. The improved failure mode and effects analysis with fuzzy evaluation method is conducted to determine the weak link of processes and develop process optimisation measures. As a practical application, the quality control of the propulsion shafting installation is conducted using the method, which proves its feasibility and effectiveness.

In order to solve the problem of obstacle avoidance for unmanned surface vehicles (USV), based on the classic RRT algorithm and Velocity Obstacle principle, an improved RRT algorithm is proposed. For the situation of the extension direction of the parent node inside the collision cone in the EXTEND operation, ‘obstacle repellent vector’ and ’collision risk index’ are presented, making the extension direction of the search tree have the tendency to move away from obstacle. Meanwhile for the problem of the real time performance of the algorithm and path oscillation, ‘target attraction vector’ and waypoint corner constraint are introduced to accelerate the convergence of the algorithm and improve the quality of path point. Path planning experiment results show that the improved algorithm has better real-time character. Path tracking experiment results based on 3-DOF ship nonlinear dynamic model reveal that the collision-free paths generated by improved RRT algorithm are smoother and the navigation time is shorter, which are of great significance for practical engineering application.

Accurate prediction of vessel traffic flow is crucial for ensuring the safety of inland river shipping and enhancing the efficiency of traffic operations. Inland vessel traffic flow typically exhibits significant complexity and spatio-temporal dynamic characteristics. To address these challenges, this paper proposes a Global-Local Spatiotemporal Transformer (GL-STFormer) deep learning model. The Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm is utilized to decompose the original data into multi-feature inputs, effectively mitigating data non-stationarity. The model integrates Gated Recurrent Units (GRU) with a self-attention mechanism to extract temporal features of traffic patterns. The multi-head attention and local masking mechanisms of the Transformer model are employed to extract global and local spatial dependencies. Furthermore, the Whale Optimization Algorithm (WOA) is applied to optimize the model’s hyperparameters. This study employs real-world Automatic Identification System (AIS) data from the Nantong waters of the Yangtze River for experimental validation. The results show that the proposed method significantly outperforms various baseline models in inland vessel traffic flow prediction. This study provides scientific support for precise traffic prediction and offers novel insights for the intelligent development of dynamic waterway traffic management.