False Data Injection Attacks (FDIA) on ship Direct Current (DC) microgrids may result in the priority trip of a large load, a black-out, and serious accidents of ship collisions when maneuvering in the port. The key of the prevention of FDIA is the detection of and countermeasures to false data. In this paper, a defense strategy is developed to detect and mitigate against FDIA on ship DC microgrids. First, a DC bus voltage estimator is trained with an Artificial Neural Network (ANN) model. The error between the estimate value and the measure value is compared with a threshold generated from history data to detect the occurrence of FDIA. Combined with the correlation of artificial neural network inputs, bad data are identified and recovered. The method is tested under six cases with different network and physical disturbances in Matlab/Simulink. The results show that the method can identify and mitigate the FDIA effectively; the error of identifying FDIA by ANN is less than 0.5 V. Therefore, the deviation between the actual bus voltage and the reference voltage is less than 0.5 V.

In the engine room of intelligent ships, visual recognition is an essential technical precondition for automatic inspection. At present, the problems of visual recognition in marine engine rooms include missing detection, low accuracy, slow speed, and imperfect datasets. For these problems, this paper proposes a marine engine room equipment recognition model based on the improved You Only Look Once v5 (YOLOv5) algorithm. The channel pruning method based on batch normalization (BN) layer weight value is used to improve the recognition speed. The complete intersection over union (CIoU) loss function and hard-swish activation function are used to enhance detection accuracy. Meanwhile, soft-NMS is used as the non-maximum suppression (NMS) method to reduce the false rate and missed detection rate. Then, the main equipment in the marine engine room (MEMER) dataset is built. Finally, comparative experiments and ablation experiments are carried out on the MEMER dataset to verify the strategy’s efficacy on the model performance boost. Specifically, this model can accurately detect 100.00% of diesel engines, 95.91% of pumps, 94.29% of coolers, 98.54% of oil separators, 64.21% of meters, 60.23% of reservoirs, and 75.32% of valves in the actual marine engine room.

These years, the connection of distributed generation (DG) to active distribution systems or microgrid has been widely used. The DGs integration and the novel optimization algorithms, makes the realization of optimal power flow (OPF) at the distribution level feasible. OPF not only reduces system power losses, but also decreases the DGs generation costs; simultaneously, the new control strategy improves the voltage profiles, which is a significant factor of power qualities. In this paper, a multi-objective function is converted into a single objective problem that defines a nonlinear power flow for optimization. Cuckoo Search (CS) algorithm is put into used.

Cyber security has drawn more attention in the research of intelligent and unmanned ships. The remote control command transmitted in time sequence has a high similarity. This makes the ships more vulnerable to cyber-attacks, especially when they are controlled remotely. Aiming at the defense of Differential Fault Attack (DFA), this paper improved the SM4 algorithm in the phase of the S-box generation and circular encryption. The Wasserstein GAN Gradient Penalty (WGAN-GP) is used to generate S-boxes dynamically to confuse differential distribution tables. After the round encryption, the combination transformation is further applied to prevent from DFA. The corresponding symmetric decryption algorithm is also developed. Simulation result shows that the generated S-box meets the cryptography criteria and the combined transformation effectively hides the sensitive information in output ciphertext and guards against the DFA.

To enable early identification of failure risks in ship systems and equipment, a dynamic cloud center of gravity model is developed for real-time system-level health assessment. First, the Functional Analysis System Technique (FAST) was applied to decompose the operational functions and dependencies of the intelligent machinery room system, enabling the structured establishment of a hierarchical evaluation index system. The comprehensive weight is derived through synergistic application of the fuzzy set (FS) theory and entropy weight. This process integrated expert-defined functional boundaries with measurable parameters critical to system performance. Then, an improved cloud center of gravity method based on the Gaussian cloud model and sliding time window method is used for the system’s adaptive health value calculation. The dynamic health model can achieve continuous online assessment and track the further evolution of the system. Finally, the proposed model is applied to the Fuel Oil Supply System (FOSS). The integration of system performance output and disassembly inspection results demonstrates that the method proposed in the article more accurately reflects the true health status changes in the system when mapping health values.

With the development of ship intelligence, the frequency of upgrading the marine engine room simulator, which is essential for crew training, has increased. Traditionally, the data processing unit (DPU) of the marine engine room simulator is upgraded by manually downloading the firmware. This makes the hardware maintenance high-cost. In this paper, we first propose a WAN-based firmware upgrade system to enable secure over-the-air upgrades of DPUs and reduce operation and maintenance costs. A distributed hardware structure is given to manage DPU in the simulator via the Internet. We have designed two methods of firmware upgrades, automatic upgrades and remote upgrades. In automatic upgrades, the DPU can download new firmware upgrades from the web server through the router. By designing a series of mechanisms including code rollback, code backup and code confirmation, the In-Application Programming (IAP) technique is realized through the Internet. Firmware upgrades have good fault tolerance mechanisms to ensure that the emulator can still work in the event of an upgrade error. In remote upgrades, we upgrade the DPU firmware through the remote control center. We assessed the performance of the system by measuring the success rate of DPU upgrades, upgrade time and performance after the upgrade. The results show that the DPU upgrade success rate is close to 100% and performance is as good as expected. The results show that the remote firmware upgrade system proposed in this paper is reliable and practical.