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SOPRENE: Assessment of the Spanish Armada’s Predictive Maintenance Tool for Naval Assets

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http://hdl.handle.net/2183/28448
Atribución 4.0 Internacional (CC BY 4.0)
Except where otherwise noted, this item's license is described as Atribución 4.0 Internacional (CC BY 4.0)
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Title
SOPRENE: Assessment of the Spanish Armada’s Predictive Maintenance Tool for Naval Assets
Author(s)
Fernández Barrero, David
Fontenla-Romero, Óscar
Lamas-López, Francisco
Novoa-Paradela, David
R-Moreno, María
Sanz, David
Date
2021
Citation
Fernández-Barrero, D.; Fontenla-Romero, O.; Lamas-López, F.; Novoa-Paradela, D.; R-Moreno, M.D.; Sanz, D. SOPRENE: Assessment of the Spanish Armada’s Predictive Maintenance Tool for Naval Assets. Appl. Sci. 2021, 11, 7322. https://doi.org/10.3390/app11167322
Abstract
[Abstract] Predictive maintenance has lately proved to be a useful tool for optimizing costs, performance and systems availability. Furthermore, the greater and more complex the system, the higher the benefit but also the less applied: Architectural, computational and complexity limitations have historically ballasted the adoption of predictive maintenance on the biggest systems. This has been especially true in military systems where the security and criticality of the operations do not accept uncertainty. This paper describes the work conducted in addressing these challenges, aiming to evaluate its applicability in a real scenario: It presents a specific design and development for an actual big and diverse ecosystem of equipment, proposing an semi-unsupervised predictive maintenance system. In addition, it depicts the solution deployment, test and technological adoption of real-world military operative environments and validates the applicability.
Keywords
Predictive maintenance
Behavioural anomaly detection
Machine learning
Deep learning
Warships
 
Editor version
https://doi.org/10.3390/app11167322
Rights
Atribución 4.0 Internacional (CC BY 4.0)

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