Andrea Coraddu

Machine learning approaches for improving condition-based maintenance of naval propulsion plants

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.

Numerical investigation on ship energy efficiency by Monte Carlo simulation

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.

Vessel monitoring and design in industry 4.0: A data driven perspective

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 vessels 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 ships 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.

Vessels Fuel Consumption: A Data Analytics Perspective to Sustainability

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.

Condition-Based Maintenance of Naval Propulsion Systems: Data Analysis with Minimal Feedback

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.

Marine Safety and Data Analytics: Vessel Crash Stop Maneuvering Performance Prediction

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.