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.

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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.

This paper presents a time-dependent biofouling growth model which enables prediction of the effect of biofouling on ship resistance and powering for day-to-day evaluation. Initially, antifouling coating tests data were employed in the model to predict coating performance over time by considering the ship operating profile and shipping route. Based on the equivalent sand roughness heights found in literature, time-dependent biofouling growth predictions were turned into equivalent sand roughness heights. Then, the provided roughness functions for different surface conditions as well as the predicted equivalent sand roughness heights were employed in Granville's similarity law scaling to investigate the effect of roughness on full-scale ship resistance.Then, the model was tested through one-year long operation data of a 176 m long tanker measured by on-board systems to validate the model. Percentage increase in frictional resistance of the 176 m long tanker was predicted to be ~32%. Results were compared and validated using real data. Secondly, a case study was performed using noon-report data for 3years operation of a 258 m long crude-oil carrier. Increase in effective power of the ship was predicted to be ~25%. Finally, the predictions were compared to ship performance reports that were provided by the ship operator.