The prediction of ship motions and quiescent periods, is of paramount importance for the maritime industry. The capability to predict these events sufficiently in advance has the potential to improve the safety and efficiency of several marine operations, such as landing and take-off on aircraft carriers, transfer of cargo, and mating operations between ships. Several models have been proposed in the literature for the prediction of ship motions and quiescent period. This work will review them by first grouping them into three main categories (i.e., physical, data-driven, and hybrid models) and then by detailing the most recent and relevant ones describing the advantages and disadvantages of each approach. Review concludes with the open problems and future perspectives of this important field of research.

@en

In offshore maritime operations, automated systems capable of maintaining the vessel's position and heading using its own propellers and thrusters to compensate exogenous disturbances, like wind, waves, and currents, are referred to as marine dynamic positioning (DP) systems. DP systems play a central role in several marine operations, such as drilling, pipe-laying, coring, and ocean observation. These operations are the primary cause of fuel consumption, having a strong impact on the overall footprint of the vessel. For this reason, we will face the problem of optimal thrust allocation of an over-actuated vessel to maintain position and heading with minimal fuel consumption. State-of-the-art approaches simplify this problem by roughly approximating it and obtain a simple, mostly convex, optimization problem that can be solved in near-real time by the automation system. In this article, we improve current approaches with the following contributions. We will exploit a higher fidelity representation of the physical system, and we will manipulate the resulting optimization problem accordingly, to allow for near-real-time solutions on conventional computing platforms on-board. We evaluate the quality of the proposal with a case study on a drilling unit equipped with six thrusters. The results will show that it is possible to achieve up to 5% of fuel savings with respect to conventional approaches. Note to Practitioners - This article was motivated by the problem of minimizing fuel consumption in thrust allocation of DP systems. The current approaches simplify this issue by adopting simpler, yet related, optimization problems as surrogates, keeping the problem tractable for near-real-time control. We propose, instead, to solve the original problem with state-of-the-art modelization of the physical system and exploit reasonable and theoretical proprietaries to achieve optimal solutions in near-real-time. The results on a drilling unit will show additional fuel savings of up to 5% with respect to alternative state-of-the-art approaches.

@en

The maintenance of the several components of a Ship Propulsion Systems is an onerous activity, which need to be efficiently programmed by a shipbuilding company in order to save time and money. The replacement policies of these components can be planned in a Condition-Based fashion, by predicting their decay state and thus proceed to substitution only when really needed. In this paper, authors propose several Data Analysis supervised and unsupervised techniques for the Condition-Based Maintenance of a vessel, characterised by a combined diesel-electric and gas propulsion plant. In particular, this analysis considers a scenario where the collection of vast amounts of labelled data containing the decay state of the components is unfeasible. In fact, the collection of labelled data requires a drydocking of the ship and the intervention of expert operators, which is usually an infrequent event. As a result, authors focus on methods which could allow only a minimal feedback from naval specialists, thus simplifying the dataset collection phase. Confidentiality constraints with the Navy require authors to use a real-data validated simulator and the dataset has been published for free use through the OpenML repository.

@en

The behavior and interaction of the main components of Ship Propulsion Systems cannot be easily modeled with a priori physical knowledge, considering the large amount of variables influencing them. Data-Driven Models (DDMs), instead, exploit advanced statistical techniques to build models directly on the large amount of historical data collected by on-board automation systems, without requiring any a priori knowledge. DDMs are extremely useful when it comes to continuously monitoring the propulsion equipment and take decisions based on the actual condition of the propulsion plant. In this paper, the authors investigate the problem of performing Condition-Based Maintenance through the use of DDMs. In order to conceive this purpose, several state-of-the-art supervised learning techniques are adopted, which require labeled sensor data in order to be deployed. A naval vessel, characterized by a combined diesel-electric and gas propulsion plant, has been exploited to collect such data and show the effectiveness of the proposed approaches. Because of confidentiality constraints with the Navy the authors used a real-data validated simulator and the dataset has been published for free use through the UCI repository.

@en

The shipping industry is today increasingly concerned with challenges related with sustainability. CO $$:2$$ emissions from shipping, although they today contribute to less than 3% of the total anthropogenic emissions, are expected to rise in the future as a consequence of increased cargo volumes. On the other hand, for the 2 $$^\circ $$ C climate goal to be achieved, emissions from shipping will be required to be reduced by as much as 80% by 2050. The power required to propel the ship through the water depends, among other parameters, on the trim of the vessel, i.e. on the difference between the ship’s draft in the fore and the aft of the ship. The optimisation of the trim can, therefore, lead to a reduction of the ship’s fuel consumption. Today, however, the trim is generally set to a fixed value depending on whether the ship is sailed in loaded or ballast conditions, based on results performed on model tests in basins. Nevertheless, the on-board monitoring systems, which produce a huge amount of historical data about the life of the vessels, lead to the application of state of the art data analytics techniques. The latter can be used to reduce the vessel consumption by means of optimising the vessel operational conditions. In this book chapter, we present the potential of data-driven based techniques for accurately predicting the influence of independent variables measured from the on board monitoring system and the fuel consumption of a specific case study vessel. In particular, we show that gray-box models (GBM) are able to combine the high prediction accuracy of black-box models (BBM) while reducing the amount of data required for training the model by adding a white-box model (WBM) component. The resulting GBM model is then used for optimising the trim of the vessel, suggesting that between 0.5 and 2.3% fuel savings can be obtained by appropriately trimming the ship, depending on the extent of the range for varying the trim.

@en

Induction motors are fundamental components of several modern automation system, and they are one of the central pivot of the developing e-mobility era. The most vulnerable parts of an induction motor are the bearings, the stator winding and the rotor bars. Consequently, monitoring and maintaining them during operations is vital. In this work, authors propose an Induction Motors bearings monitoring tool which leverages on stator currents signals processed with a Deep Learning architecture. Differently from the state-of-the-art approaches which exploit vibration signals, collected by easily damageable and intrusive vibration probes, the stator currents signals are already commonly available, or easily and unintrusively collectable. Moreover, instead of using now-classical data-driven models, authors exploit a Deep Learning architecture able to extract from the stator current signal a compact and expressive representation of the bearings state, ultimately providing a bearing fault detection system. In order to estimate the effectiveness of the proposal, authors collected a series of data from an inverter-fed motor mounting different artificially damaged bearings. Results show that the proposed approach provides a promising and effective yet simple bearing fault detection system.

@en

The continuous increase of marine traffic and the entry of autonomous ships into the market is urging an improvement in safety measures to guarantee avoidance of collisions between moving objects at sea. This rise in automated maneuverability requires gaining further insight in the vessel’s behavior. The ship design has to ensure that the vessel is controllable and capable of maneuvering securely, even at critical operating conditions. Crash stop maneuvering performance is one of the key indicators of the vessel’s safety properties for designers and shipbuilders. Many factors affect this performance, from the hull design to the environmental conditions; hence, it is non-trivial to assess them accurately during the preliminary design stages. In this paper, the authors focus on predicting accurately and with minimal computational effort the crash stop characteristics of a vessel in the design stage, for the preliminary assessment of safety requirements imposed by the classification societies. The crash stop prediction model of the said vessel can be utilized in combination with collision avoidance algorithms. The authors propose a new data-driven method, based on the popular Random Forests learning algorithm, for predicting the crash stop maneuvering performance. Results from full-scale measured data show the effectiveness of the proposed method.@en

In this paper the authors investigate the problems of predicting the fuel consumption and of providing the best value for the trim of a vessel in real operations based on data measured by the onboard automation systems. Three different approaches for the prediction of the fuel consumption are compared: White, Black and Gray Box Models. White Box Models (WBM) are based on the knowledge of the physical underling processes. Black Box Models (BBMs) build upon statistical inference procedures based on the historical data collection. Finally, the authors propose two different Gray Box Model (GBM) which are able to exploit both mechanistic knowledge of the underlying physical principles and available measurements. Based on these predictive models of the fuel consumption a new strategy for the optimisation of the trim of a vessel is proposed. Results on real world operational data show that the BBM is able to remarkably improve a state-of-the-art WBM, while the GBM is able to encapsulate the a-priory knowledge of the WBM into the BBM so to achieve the same performance of the latter but requiring less historical data. Moreover, results show that the GBM can be used as an effective tool for optimising the trim of a vessel in real operational conditions.

@en

Crash stop maneuvering performance is one of the key indicators of the vessel safety properties for a shipbuilding company. Many different factors affect these performances, from the vessel design to the environmental conditions, hence it is not trivial to assess them accurately during the preliminary design stages. Several first principal equation methods are available to estimate the crash stop maneuvering performance, but unfortunately, these methods usually are either too costly or not accurate enough. To overcome these limitations, the authors propose a new data-driven method, based on the popular Random Forests learning algorithm, for predicting the crash stopping maneuvering performance. Results on real-world data provided by the DAMEN Shipyards show the effectiveness of the proposal.

@en

Availability, reliability and economic sustainability of naval propulsion plants are key elements to cope with because maintenance costs represent a large slice of total operational expenses. Depending on the adopted strategy, impact of maintenance on overall expenses can remarkably vary; for example, letting an asset running up until breakdown can lead to unaffordable costs. As a matter of fact, a desideratum is to progress maintenance technology of ship propulsion systems from breakdown or preventive maintenance up to more effective condition-based maintenance approaches. The central idea in condition-based maintenance is to monitor the propulsion equipment by exploiting heterogeneous sensors, enabling diagnosis and, most of all, prognosis of the propulsion system's components and of their potential future failures. The success of condition-based maintenance clearly hinges on the capability of developing effective predictive models; for this purpose, effective use of machine learning methods is proposed in this article. In particular, authors take into consideration an application of condition-based maintenance to gas turbines used for vessel propulsion, where the performance and advantages of exploiting machine learning methods in modeling the degradation of the propulsion plant over time are tested. Experiments, conducted on data generated from a sophisticated simulator of a gas turbine, mounted on a Frigate characterized by a COmbined Diesel eLectric And Gas propulsion plant type, will allow to show the effectiveness of the proposed machine learning approaches and to benchmark them in a realistic maritime application.

@en

The main purpose of this work is to build a data driven model to create realistic operating profiles in order to assess and compare different design solutions. The proposed approach takes advantage on the new generation of automation systems which allow gathering a large amount of data from on-board machinery. A data driven modeling of the operational profiles of the vessel (and in general of the fleet) could provide a tool both to diagnose and predict the vessel's state (e.g. for condition based maintenance purposes), for improving the performance and the efficiency of the vessel, and for improving design solutions. The diagnosis and prognosis of the ship's performance can be used as decision support in determining when actions to improve performance should be taken. The developed model will be tested on a real DAMEN vessel where on-board sensors data acquisitions are available from the automation system.

@en

The main purpose of thiswork is to build a data driven model to create realistic operating profiles in order to assess and compare different design solutions. The proposed approach takes advantage on the new generation of automation systems which allow gathering a large amount of data from on-board machinery. Data driven models are built upon statistical inference procedures based on the historical data collection. The advantage of these methods is that there is no need of any a-priory knowledge of the underlying physical system. Furthermore, thanks to the nature of these approaches, it is possible to exploit even data from sensors that could contain some kind of hidden information that cannot be easily extracted with a parametric approach. The use of these tools is nowadays made possible since such information is digitally available from different sources: (i) data stored on board of vessels; (ii) Automatic Identification System (AIS) data available through the internet. A data driven modelling of the operational profiles of the vessel (and in general of the fleet) could provide a tool both to diagnose and predict the vessel’s state (e.g. for condition based maintenance purposes), for improving the performance and the efficiency of the vessel, and for improving design solutions. The diagnosis and prognosis of the ship’s performance can be used as decision support in determining when actions to improve performance should be taken. The developed model will be tested on a real DAMEN vessel where on-board sensors data acquisitions are available from the automation system.@en

In this paper authors investigate the problem of predicting the fuel consumption of a vessel in real scenario based on data measured by the onboard automation systems. The goal is achieved by exploiting three different approaches: White, Black and Gray Box Models. White Box Models (WBM) are based on the knowledge of the physical underling processes. Black Box Models (BBMs) build upon statistical inference procedures based on the historical data collection. Author proposal is a Gray Box Model (GBM) which is able to exploit both mechanistic knowledge of the underlying physical principles and available measurements. Results on real world data shows that the BBM is able to remarkably improve a state-of-the-art WBM, while the GBM is able to encapsulate the a-priory knowledge of the WBM into the BBM so to achieve the same performance of the latter but requiring less historical data.

@en

Economic sustainability of running Naval Propulsion Plants is a key element to cope with, and maintenance costs represent a large slice of total operational expenses: last decades' approaches, based on a repairing-replacing methodology, are being trespassed by more effective approaches, relying on effective continuous monitoring of assets wear. In this framework, Condition-Based Maintenance (CBM) is becoming key thanks to the enhancing capabilities of monitoring the propulsion equipment by exploiting heterogeneous sensors: this enables diagnosis and prognosis of the propulsion system's components and of their potential future failures. The success of CBM is based on the capability of developing effective predictive models, for which purpose state-of-the-art Machine Learning (ML) methods must be developed. Nevertheless, testing the performance of ML models for CBM purposes is not straightforward, mostly due to the lack of publicly available datasets for benchmarking purposes: thus, we present in this work a new dataset, that will be freely distributed to the community working on ML models for CBM, generated from an accurate simulator of a naval vessel Gas Turbine propulsion plant. The latter is then used for benchmarking the effectiveness of two state-of-the-art ML techniques in the considered maritime domain.

@en