To realize space-sea integrated communication between multiple ships under the BeiDou-3 satellite communication topology, this paper proposes a new satellite-ship autonomous communication system based on a deep learning anomaly detection algorithm. A deep learning algorithm combining coarse-grained detection (piecewise oversampling principal component analysis, POsPCA) and fine-grained sorting (VAE and differential ARIMA joint model) was established to realize two-way autonomous communication between BeiDou-3 satellites and ships. Specifically, a segmented oversampling principal component analysis algorithm was proposed to analyze anomalous BeiDou-3 short message data segments in the coarse-grained detection stage. In the fine-grained sorting stage, a joint fusion reconstruction and prediction model was proposed to calculate the fine-grained anomaly score. The autonomous communication system establishes the communication priority of multiple ships based on this score. Using the experimental platform independently built for this study to verify the algorithm performance, the proposed algorithm effectively realizes the priority ranking of anomaly scores by detecting abnormal data segments, effectively saving 64.4% of BeiDou-3 satellite short message communication resources. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

The transmission standard and format of short-message communication in the BeiDou satellite-navigation-system are open, the civilian channel adopts a direct-transmission plaintext mechanism, and the content of short messages is at risk of data leaks. Aiming at addressing the problem of safely transmitting BeiDou short messages, this study proposes a CGAN BeiDou satellite short-message-encryption scheme, using ship PVT (position, speed, and time). Various BeiDou commands with ship position, speed, or time are employed as the input. The two communicating parties use the CGAN (conditional generative adversarial network) confrontation mechanism to encrypt and decrypt the ship-PVT information and generate a symmetrically encrypted key, while the receiver parses only the sender data within the specified PVT-range. Additionally, because the BeiDou system has a positioning error and transmission delay, and considering the ship mobility, the concept of a dynamic tolerance region is introduced at the receiver, to improve the scheme’s decryption success-rate. Finally, the proposed scheme is verified, using simulation and experiments. The proposed algorithm achieves good security, with acceptable efficiency. Furthermore, the experimental platform built by this study is used to prove the feasibility of the scheme applied to BeiDou short-message encryption and decryption. © 2022 by the authors.

舰船电力系统环形网络故障重构本质上是带约束的多目标非线性组合优化问题。为了解决舰船电力系统发生故障时的供电恢复问题,提出了一种改进双粒子群优化算法进行求解。此算法分为主、辅两个粒子群,主粒子群改进了种群初始化、自适应调整惯性权重和学习因子,提高了主粒子群算法的全局寻优能力。同时,辅助粒子群还采用改进的混沌局部搜索策略,增强了种群多样性及局部寻优能力,有效地解决了粒子群算法中容易陷入局部极值的问题。通过系统仿真,分别将几种不同的优化算法进行比较。结果表明该算法具有很高的搜索效率和寻优能力,能有效地提高故障恢复的速度与精度,在处理舰船电力系统网络故障重构方面具有较好的效果。

Obtaining voyage data through a vessel monitoring system (VMS) and transmitting it to a remote monitoring center through satellites is an important link to realize the information perception of smart ships. However, improving data quality to ensure the integrity of information is still an open question. Taking into account the complex and harsh marine environment, a BeiDou satellite transmission link will inevitably suffer the challenge of packet loss. Therefore, this paper proposes a novel BeiDou satellite transmission framework with missing package imputation to improve the information perception ability of smart ships. In particular, we propose a two-stage missing packet imputation strategy (T-MPI). First, a bidirectional recurrent neural network (Bi-RNN) based on temporal view imputation is designed to learn the time correlation of the data. Second, considering the attributive view imputation of multivariate data, an autoencoder with a linear aggregation module (LA-AE) is proposed to capture sudden changes in the ship's complex sailing state. An experimental platform was established to verify the developed framework, and real freighter transportation data were used to prove the effectiveness of our proposed imputation strategy under complex sailing conditions.

A satellite information transmission network is different from a ground network. The link distance between the satellite and the ground is large, and the communication between them is affected by the ionosphere. The ground congestion control algorithm cannot provide fast and stable responses to short-term burst flows. Therefore, aiming at the problem of large propagation delays and the complex environments of satellite-to-ground links, this paper investigates the Vegas congestion mechanism of the Space Communication Protocol Standard Transport Protocol (SCPS-TP), incorporates the interference affecting satellite communication links into a discrete model, combines interference estimation with controller design, proposes a reaching law and sliding mode-based predictive control algorithm for satellite communication, and suggests the stability of the introduced algorithm. The simulation results demonstrate the anti-jamming ability of the proposed algorithm for addressing short-term burst flows, and the prediction mechanism of the algorithm improves the utilization of satellite network bandwidth.

BeiDou-3 satellites have short-message communication capabilities, which are not available in other navigation systems. However, the information transmission capacities of BeiDou-3 satellites are limited, which means that the string length of a single transmission is restricted. To improve transmission performance, this paper proposes a lossy data compression algorithm to transmit ship data. First, due to the large amount of measurement noise in the ship data and the difficulty of adaptively adjusting the compression threshold of the swinging door trend, a dynamic threshold adjustment strategy based on model-free adaptive control is designed. Furthermore, because the performance of the strategy depends on the initial value, chaos multilayer particle swarm optimization is employed to optimize the strategy and achieve better generalization and convergence performance. In addition, this paper introduces an experimental system and uses real ship motor vibration and electromagnetic radiation data to verify the compression algorithm. The experimental results indicate that the proposed algorithm displays reasonable performance. Presently, the system designed in this paper has been operating stably for more than six months.

针对舵桨联合操纵的船舶航速/航向协调容错控制问题,结合舵桨功能有效性,提出了一种非线性广义扰动观测器,无需扰动先验信息在线补偿系统的扰动状态。基于广义扰动补偿和自适应估计提出了一种自适应滑模容错控制算法,通过实时估计广义扰动导数的范数上界消除扰动重构误差,同时设计了具有故障惩罚作用的伪逆优化分配策略,引入执行器有效性矩阵来修正故障执行器的优先作用等级,实现了冗余舵桨系统故障运行模式下的航速/航向跟踪、广义扰动补偿和执行器能耗最优化。仿真实验结果验证了所提的广义扰动观测器和自适应滑模容错控制系统的有效性。

由于全电力船舶推进系统结构组成复杂,且海洋环境恶劣,面临着系统故障频发、安全苛求等问题。针对传统风险优先数评估存在的缺陷,提出了一种基于模糊置信理论的故障模式影响模糊评估方法。利用信息熵与定性分析融合的综合权重确定专家

This study proposed a fault diagnosis method of a shipboard medium-voltage DC (MVDC) power system based on Noise-Assisted Multivariate Empirical Mode Decomposition (NA-MEMD) and Multilevel Iterative – LightGBM (MI-LightGBM), which overcomes the limitations of the existing fault diagnosis methods in this regard, such as relies heavily on the relay or slow training process. MI-LightGBM is proposed to solve the problem of unbalanced training samples caused by the difficulty in obtaining fault samples in practical engineering. First, NA-MEMD was adopted to pre-process the voltage signals, which were decomposed into a set of components called Intrinsic Mode Functions (IMFs) according to the local characteristic time scales of the original signals. The energy moment of each order IMF was calculated as fault feature vector to train the MI-LightGBM model, which led to the development of a high-precision fault classifier. A model of a shipboard MVDC power system was established using the AppSIM Real-Time Simulator. Simulations were performed on earth fault and short-circuit fault at the generator output and DC cable. Compared with the existing fault diagnosis methods, the proposed method is simple to use and save more than half of the training time while maintaining high diagnostic performance, which is more suitable for engineering applications.
© 2020 Elsevier Ltd