Ship diesel generator systems are critical to ship navigation. However, due to the harsh marine environment, the systems are prone to failures, and traditional fault diagnosis methods are difficult to meet requirements regarding accuracy, robustness, and reliability. For this reason, this paper proposes a fault diagnosis method for a ship diesel generator system based on the IVY algorithm-optimized random forest (IVY-RF). Firstly, a model of a ship diesel generator system was constructed using MATLAB/Simulink, and the operation data under fault and normal working conditions were collected. Then, the data were preprocessed and time-domain features were extracted. Finally, the IVY-optimized random forest model was used to identify, diagnose, and classify faults. The simulation results show that the IVY-RF method could identify faulty and normal states with 100% accuracy and distinguish 12 types with 100% accuracy. Compared to seven different algorithms, the IVY-RF improved accuracy by at least 0.17% and up to 67.45% on the original dataset and by at least 1.19% and up to 49.40% in a dataset with 5% noise added. The IVY-RF-based fault diagnosis method shows excellent accuracy and robustness in complex marine environments, providing a reliable fault identification solution for ship power systems.

In order to solve a certain type of Electro-Hydrostatic Actuators (EHA) hydraulic cylinder small cavity buffer end impact problem, based on AMESim to establish a hydraulic cylinder small cavity buffer machine–hydraulic joint simulation model. First, four important structural parameters, namely, the fitting clearance G of the buffer sleeve and buffer hole, the fixed orifice D, the wedge face angle $\theta$, and the wedge face length L₁ were selected to analyze their influence on the pressure of the buffer chamber and the end speed of the piston. Second, enhanced Social Behavior Optimization (SBO) was used to optimize the back-propagation neural network (BP) model to construct a prediction model for the buffer time T of the small chamber of the hydraulic cylinder, the end-piston speed V_e, the rate of change of the end-piston speed V_r, and the return speed of the hydraulic oil V_h. The SBO–BP model performed well in several key performance evaluation metrics, showing better prediction accuracy and generalization performance. Finally, the multi-objective Non-dominated Sorting Genetic Algorithm II (NSGA-II) was used to optimize the hydraulic cylinder small-cavity buffer structure using the multi-objective NSGA-II with the objectives of the shortest buffer time, the minimum end-piston speed, the minimum change rate of the end-piston speed, and the minimum hydraulic oil reflux speed. The optimized design reduced the piston end speed from 0.060 m/s to 0.032 m/s, corresponding to a 46.7% improvement. The findings demonstrate that the proposed hybrid optimization approach effectively alleviates the end-impact problem of EHA small-cavity buffers and provides a novel methodology for achieving high-performance and reliable actuator designs.

At present, the development of the global energy internet is occurring in depth and the construction of a distributed power supply is rapid, and the energy router (ER), as a key device for integrating energy flow and information flow, has important application value in microgrids. In this paper, a multi-port energy router based on a 710 V DC bus is designed and developed with a modular structure design, including core components such as a total controller, a power converter, a hybrid energy storage system, and an auxiliary power supply. Flexible access and the management of multiple-voltage-level ports (690 V AC, 380 V AC, 220 V DC, and 24 V DC) are realized through rational topology design. The test results of the device show that the system performance indexes meet the design requirements. The operation is stable and reliable, displaying strong practical engineering value, and at the same time provides a technical solution that can be borrowed for other special scenarios such as the microgrid system.

As the scale of shipboard power systems expands, their vulnerability becomes increasingly prominent. Identifying vulnerable points in ship power grids is essential for enhancing system stability, optimizing overall performance, and ensuring safe navigation. To address this issue, this paper proposes an algorithm based on fuzzy local dimension and topology (FLDT). The algorithm distinguishes contributions from nodes at different radii and within the same radius to a central node using fuzzy sets, and then derives the final importance value of each node by combining the local dimension and topology. Experimental results on nine datasets demonstrate that the FLDT algorithm outperforms degree centrality (DC), closeness centrality (CC), local dimension (LD), fuzzy local dimension (FLD), local link similarity (LLS), and mixed degree decomposition (MDD) algorithms in three metrics: network efficiency (NE), largest connected component (LCC), and monotonicity. Furthermore, in a ship power grid experiment, when 40% of the most important nodes were removed, FLDT caused a network efficiency drop of 99.78% and reduced the LCC to 2.17%, significantly outperforming traditional methods. Additional experiments under topological perturbations—including edge addition, removal, and rewiring—also show that FLDT maintains superior performance, highlighting its robustness to structural changes. This indicates that the FLDT algorithm is more effective in identifying and evaluating vulnerable points and distinguishing nodes with varying levels of importance.

Accurate identification of key nodes in complex networks is vital for optimizing system robustness and controlling information spread. Existing centrality metrics struggle to balance the continuous extraction of global topological features with the fine-grained perception of local structures, while traditional heuristic algorithms also face severe resolution limitations. To address these issues, this paper proposes a node importance evaluation method based on fractional-order topological propagation and local information entropy (FSEC). This method overcomes the limitations of discrete integer-order propagation inherent in traditional graph walks. It constructs a continuous fractional-order topological propagation operator within the spectral graph theory framework. This enables the smooth projection of node degree features into the global topological space, thereby yielding high-order global impact factors. Furthermore, an information theory mechanism is introduced to quantify the probability distribution of a node’s information contribution within its local neighborhood. The local structural information entropy is then calculated to reflect the node’s asymmetric control over micro-level information flow. Deliberate attack simulations were conducted on nine real-world networks and three types of artificial network models. The results show that the proposed FSEC algorithm significantly outperforms baseline algorithms like Autoencoder and Graph Neural Network (AGNN), Degree Centrality, k-shell, PageRank, and Mixed Degree Decomposition (MDD) in degrading the largest connected component (LCC) and global network efficiency (NE). The proposed method also achieves the minimum Area Under the Curve (AUC) values globally. Its monotonicity is slightly lower than that of AGNN but superior to all other baseline algorithms. In addition, SIR simulations further confirm the effectiveness of the FSEC method. This approach successfully resolves the ranking tie problem among nodes in the same topological layer.

The vibration signals of Electro-Hydrostatic Actuators (EHAs) exhibit strong non-linearity and non-stationarity, particularly under complex coupling mechanisms, making the extraction of intrinsic fault features computationally challenging. Conventional deep learning approaches often lack mathematical interpretability and struggle to decouple superimposed fault signatures from incomplete datasets. To address these issues, this paper proposes the Enhanced Continuous Wavelet Transform Capsule Network (ECWTCN), an intelligent decoupled diagnosis framework designed for multiscale signal analysis. The architecture integrates a wavelet-kernel convolution layer to extract physically interpretable time–frequency features across multiple scales, effectively capturing transient impulses associated with incipient faults. Furthermore, a novel maximized aggregation routing algorithm is introduced to optimize the dynamic routing process, enhancing global feature aggregation. A distinct advantage of the ECWTCN is its capability to generalize distinct fault patterns, enabling the identification of unseen compound faults by training exclusively on normal and single-fault samples. Comparative experiments show that the proposed method delivers strong multi-label classification performance under operating condition A, achieving a Subset Accuracy of 93.7% and a Label Ranking Average Precision of 0.998. Complexity analysis further confirms the method’s efficiency in terms of FLOPs and parameter size. This work presents a robust, lightweight, and mathematically interpretable solution for the analysis of complex signals in high-reliability equipment.