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

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

Shipping is responsible for approximately the 90% of world trade leading to significant impacts on the environment. As a consequence, a crucial issue for the maritime industry is to develop technologies able to increase the ship efficiency, by reducing fuel consumption and unnecessary maintenance operations. For example, the marine fouling phenomenon has a deep impact, since to prevent or reduce its growth which affects the ship consumption, costly drydockings for cleaning the hull and the propeller are needed and must be scheduled based on a speed loss estimation. In this work a data driven Digital Twin of the ship is built, leveraging on the large amount of information collected from the on-board sensors, and is used for estimating the speed loss due to marine fouling. A thorough comparison between the proposed method and ISO 19030, which is the de-facto standard for dealing with this task, is carried out on real-world data coming from two Handymax chemical/product tankers. Results clearly show the effectiveness of the proposal and its better speedloss prediction accuracy with respect to the ISO 19030, thus allowing reducing the fuel consumption due to fouling.@en

Traditionally, the environmental performance of marine systems in terms of exhaust emissions has never been among the primary concerns of the maritime industry. However, this situation is going to quickly change, as in the shipping sector Energy Efficiency Design and Operation IMO Indexes testify. In such a context it is worth mentioning that the greening of shipping operations can be effectively achieved by a suitable system design and energy management. Ship Energy Efficiency Management Plan (SEEMP) should be a tool to monitor and to optimize ship and fleet efficiency performance; as a consequence it will have a deep impact on the reduction not only of the exhaust gas emissions but also of the operational costs. To reach this goal energy modelling represents the keyword. In particular, the estimate by simulation, of onboard power generation, consumption and losses plays a fundamental role for driving, for instance, a decision support tool toward the optimal choices as far as energy management is concerned. To this purpose, in the paper the authors present the outcomes of the activities carried out to develop a simulation tool able to represent an overall ship system from the energetic point of view. Modelling approach and the results of numerical simulations performed by the authors to estimate vessel fuel consumption in a real case study are shown and discussed.@en

In this article, the authors present a procedure to predict the energy efficiency operational indicator by Monte Carlo simulations, estimating the total ship fuel consumption as a function of displacement and speed considered as random variables. To characterize the probability density function of displacement and speed, a complete series of operating data concerning 2 years of navigation, of a RoPax engaged in a commercial trade in the Mediterranean Sea, were collected and used.@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

The integrated use of modern seakeeping and powering computational techniques and state of the art meteo-marine forecasting and hindcasting resources has the potential of disclosing new ways to approach some relevant issues, such as for instance operations planning at sea, weather routing, in-service ship energy management and pollution control. Such an integrated approach could be profitably used during ship design for optimizing both hydrodynamic and powering characteristics too. In this paper the authors describe preliminary results of a study aimed at estimating the in-service optimal efficiency propulsion plant setting for a ship in real seaways conditions. To this aim numerical simulations of seakeeping and powering conditions of a ship along a real route in the Mediterranean sea have been performed. In the simulations detailed meteo-marine hindcasting data, consisting of wind data and complete directional waves spectra along the route, have been used. Strip theory has been applied for the seakeeping computation in order to evaluate the contribution of waves added resistance in realistic seaways. Such results have been then used in powering computation, to evaluate the dynamically evolving engine conditions in terms of fuel consumption. The results of such computations have been analyzed to evaluate the in-service optimization potentialities for the available setting of propulsion plant. Finally, heuristic hints are preliminarily suggested by the authors as a support for the use of scenario simulations with realistic meteo-marine data in the powering matching phases of the ship design process.@en

IMO is introducing two different emission indexes for a vessel: the Energy Efficiency Design Index (EEDI) and the Energy Efficiency Operational Indicator (EEOI). Both EEDI and EEOI will be probably used as part of a levy scheme to force the maritime sector to significantly reduce the carbon footprint. At present do exist few ports and/or states applying fees based on ship emissions. The reliability of a regular shipping service, or service reliability, defined as the probability to complete the link within schedule, is an important parameter that greatly influences commercial aspects, as well as the ship carbon footprint. In this paper the authors present a procedure to evaluate the EEOI indicator by Monte Carlo simulation, estimating the total ship fuel consumption as a function of the random variables displacement and speed. A complete series of operating data concerning two years of navigation, for a commercial trade in the Mediterranean Sea, were collected and used to validate the code.@en