Picture of DNA strand

Pioneering chemical biology & medicinal chemistry through Open Access research...

Strathprints makes available scholarly Open Access content by researchers in the Department of Pure & Applied Chemistry, based within the Faculty of Science.

Research here spans a wide range of topics from analytical chemistry to materials science, and from biological chemistry to theoretical chemistry. The specific work in chemical biology and medicinal chemistry, as an example, encompasses pioneering techniques in synthesis, bioinformatics, nucleic acid chemistry, amino acid chemistry, heterocyclic chemistry, biophysical chemistry and NMR spectroscopy.

Explore the Open Access research of the Department of Pure & Applied Chemistry. Or explore all of Strathclyde's Open Access research...

Condition-based maintenance of naval propulsion systems with supervised data analysis

Cipollini, Francesca and Oneto, Luca and Coraddu, Andrea and Murphy, Alan John and Anguita, Davide (2018) Condition-based maintenance of naval propulsion systems with supervised data analysis. Ocean Engineering, 149. pp. 268-278. ISSN 0029-8018

Text (Cipollini-etal-OE-2018-Condition-based-maintenance-of-naval-propulsion-systems-with-supervised-data-analysis)
Accepted Author Manuscript
License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 logo

Download (501kB)| Preview


    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.