The maritime industry widely expects to have autonomous and semiautonomous ships (autoships) in the near future. In order to operate and maintain complex and integrated systems in a safe, efficient, and cost-beneficial manner, autoships will require intelligent Prognostics and Health Management (PHM) systems. Deep learning (DL) is a potential area for this development, as it is rapidly finding applications in a variety of domains, including self-driving cars, smartphones, vision systems, and more recently in PHM applications. This paper introduces and reviews four well-established DL techniques recently applied to various practical PHM problems. The purpose is to support creativity and provide inspiration toward the PHM based on DL in autoships and the maritime industry. This paper discusses benefits, challenges, suggestions, existing problems, and future research opportunities with respect to this significant new technology.

Maintenance is the key to ensuring the safe and efficient operation of marine vessels. Currently, reactive maintenance and preventive maintenance are two main approaches used onboard. These approaches are either cost-intensive or labor-intensive. Recently, Prognostics and Health Management has emerged as an optimal way to manage maintenance operations. In such a system, fault prognostics aims to predict the remaining useful life based on the sensor measurements. In this paper, the feasibility of applying data-driven fault prognostics to marine diesel engines is investigated. Real-world run-to-failure data of two independent fault-types in two different engine load profiles are collected from a hybrid power lab. The first profile is used for training and validation, while the second is used for testing. The LSTM networks are used to construct the fault prognostics model. Experiments and comparisons are performed to obtain the optimal structure of the networks. Results show that the proposed method generalizes well on the second profile and provides remaining useful life predictions with high accuracy.

High-fidelity models capable of accurately predicting ship motion are critical for promoting innovation and efficiency in the maritime industry. However, creating an advanced model that comprehensively represents the system and its interaction with dynamic environments has always been challenging. Many models provide partial knowledge about a system. To handle the deficiency and improve model fidelity, in this artile, we propose a hybrid modeling methodology, in which prior knowledge describing the ship dynamic effects is incorporated into a data-driven calibrator, yielding a representative model with high predictive capability. Enabled by the integration of model estimated ship states into the calibrator, the informative information could be interpreted and carried forward. Simulation and full-scale experiments are conducted on the research vessel Gunnerus to exemplify the concept. A best available numerical model and a neural network are prepared to be the foundation and calibrator, respectively. Experiment results show that the cooperative model greatly improves the predictive capability of the research vessel. From the ship modeling perspective, this study provides new insights by bridging the gap between two separate domains: 1) model-based and 2) data-driven.

As the use of fossil fuels becomes more and more restricted there is a need for alternative fuels also at sea. For short sea distance travel purposes, batteries may be a solution. However, for longer distances, when there is no possibility of recharging at sea, batteries do not have sufficient capacity yet. Several projects have demonstrated the use of compressed hydrogen (CH₂) as a fuel for road transport. The experience with hydrogen as a maritime fuel is very limited. In this paper, the similarities and differences between liquefied hydrogen (LH₂) and liquefied natural gas (LNG) as a maritime fuel will be discussed based on literature data of their properties and our system knowledge. The advantages and disadvantages of the two fuels will be examined with respect to use as a maritime fuel. Our objective is to discuss if and how hydrogen could replace fossil fuels on long distance sea voyages. Due to the low temperature of LH₂ and wide flammability range in air these systems have more challenges related to storage and processing onboard than LNG. These factors result in higher investment costs. All this may also imply challenges for the LH₂ supply chain.

Enthusiasm for ship autonomy is flourishing in the maritime industry. In this context, data-driven Prognostics and Health Management (PHM) systems have emerged as the optimal way to improve operational reliability and system safety. However, further research is needed to enhance the essential actions relating to such a system. Fault detection is the first and most crucial action of any data-driven PHM system. In this study, we propose a fault-type independent spectral anomaly detection algorithm for marine diesel engine degradation in autonomous ferries. The benefits of the algorithm are verified on three fault-types where the nature of degradation differs. Both normal operation data and faulty degradation data have been collected from a marine diesel engine, using two different engine load profiles. These profiles aim to replicate real autonomous ferry crossing operations, environmental conditions the ferry may encounter. First, the data is subjected to a feature selection process to remove irrelevant and redundant features. Then, a multi-regime normalization method is performed on the data to merge the engine loads into one context. Finally, a variational autoencoder is trained to estimate velocity and acceleration calculations of the anomaly score. Generic and dynamic threshold limits are simultaneously established to detect the fault time step online. The algorithm achieved an accuracy of 97.66% in the final test when the acceleration was used as the fault detector. The results suggest that the algorithm is independent of faulttypes with different nature of degradation related to the marine diesel engine.

In this paper, we apply computational fluid dynamics (CFD) to study the thermodynamic response enhanced by sloshing inside liquefied natural gas (LNG) fuel tanks. An existing numerical solver provided by OpenFOAM is used to simulate sloshing in a model scaled tank of similar form to an LNG fuel tank. The interface area has been estimated for different sloshing regimes on three different numerical grids representing the tank in 3D. Estimating the interface area is done by performing a grid-independence study. In the most severe sloshing conditions, convergence is not achieved. By combining the results from experiments and CFD, it is found that the interface area and the condensation mass flow rate are in phase for the most severe sloshing condition. The existing CFD solver is modified to determine the pressure drop. The simulation results are compared to the experimental data, and the results are acceptable and thereby show a potential in applying CFD to predict the thermodynamic response due to sloshing. By plotting the temperature contours, indications are found that the exchange of cold bulk and saturated liquid due to sloshing has a significant influence on the thermodynamic response.