Ships are special machineries with large inertias and relatively weak driving forces. Simulating the manual operations of manipulating ships with artificial intelligence (AI) and machine learning techniques becomes more and more common, in which avoiding collisions in crowded waters may be the most challenging task. This research proposes a cooperative collision avoidance approach for multiple ships using a multi-agent deep reinforcement learning (MADRL) algorithm. Specifically, each ship is modeled as an individual agent, controlled by a Deep Q-Network (DQN) method and described by a dedicated ship motion model. Each agent observes the state of itself and other ships as well as the surrounding environment. Then, agents analyze the navigation situation and make motion decisions accordingly. In particular, specific reward function schemas are designed to simulate the degree of cooperation among agents. According to the International Regulations for Preventing Collisions at Sea (COLREGs), three typical scenarios of simulation, which are head-on, overtaking and crossing, are established to validate the proposed approach. With sufficient training of MADRL, the ship agents were capable of avoiding collisions through cooperation in narrow crowded waters. This method provides new insights for bionic modeling of ship operations, which is of important theoretical and practical significance.

Comprised of multiple belief rules that are close to human knowledge representation, the belief rule base (BRB) has the ability to make full use of both data obtained from historic records and experts’ knowledge under uncertainty. When applied in fault diagnosis, the final diagnostic results are inferred by first activating the matching degrees and activation weights, and then integrating the activated rules with the evidential reasoning algorithm. In this study, a novel BRB prediction approach is proposed for concurrent fault diagnosis. In the proposed approach, the indicative factors are modeled as attributes with customized attribute weights to represent their relevance to a known system fault and vice versa. Second, a tradeoff analysis is conducted by comparing the beliefs for different faults. The diagnosed faults with bigger beliefs within a predetermined valve would be considered as concurrent faults. To further improve the prediction accuracy, an optimization approach with the differential evolutionary algorithm as the optimization engine is designed with model constraints reflecting the complex correlations between the indicative factors and concurrent faults. A marine diesel engine fault diagnosis case is studied for validation. Case study results show that: first, both single and concurrent faults can be diagnosed with high accuracy by the proposed BRB prediction approach; second, the BRBs with optimized attributes weights has shown superior performance than with equal and fixed attribute weights; and third, the new BRB prediction approach outperforms the previous approach with multiple sub-BRBs as well as other approaches to achieve a higher modeling accuracy.

In this paper, a hybrid ship powered by diesel generator sets and power batteries in series is considered. By analyzing the characteristics of hybrid ship cycle operating conditions, the load power of the hybrid ship under load uncertainty is firstly predicted. Then, considering the economy, emissions and continuous navigation time (endurance) of the hybrid ship, an energy optimization strategy based on the predicted load power is proposed to achieve the goal of minimum fuel consumption, minimum emissions and maximum endurance of ship operation. The experimental results show that, compared with the fuzzy logic rules based strategy, the fuel economy of the ship is increased by 9.6% and the ship’s endurance is increased by 24% for the proposed strategy.

This article investigates how to make use of imperfect data gathered from different sources for inference and decision making. Based on Bayesian inference and the principle of likelihood, a likelihood analysis method is proposed for acquisition of evidence from imperfect data to enable likelihood inference within the framework of the evidential reasoning (ER). The nature of this inference process is underpinned by the new necessary and sufficient conditions that when a piece of evidence is acquired from a data source it should be represented as a normalized likelihood distribution to capture the essential evidential meanings of data. While the explanation of sufficiency of the conditions is straightforward based on the principle of likelihood, their necessity needs to be established by following the principle of Bayesian inference. It is also revealed that the inference process enabled by the ER rule under the new conditions constitutes a likelihood inference process, which becomes equivalent to Bayesian inference when there is no ambiguity in data and a prior distribution can be obtained as a piece of independent evidence. Two examples in decision analysis under uncertainty and a case study about fault diagnosis for railway track maintenance management are examined to demonstrate the steps of implementation and potential applications of the likelihood inference process.

Information fusion methods based on Dempster-Shafer evidence theory (DST) have been widely used in fault diagnosis. In DST-based methods, the monitoring information collected from sensors is modeled as multiple pieces of diagnosis evidence in the form of basic belief assignment (BBA), and Dempster’s rule is then used to combine these BBAs to obtain...