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SOPRENE: Assessment of the Spanish Armada’s Predictive Maintenance Tool for Naval Assets
dc.contributor.author | Fernández Barrero, David | |
dc.contributor.author | Fontenla Romero, Óscar | |
dc.contributor.author | Lamas López, Francisco | |
dc.contributor.author | Novoa Paradela, David | |
dc.contributor.author | María Dolores, R-Moreno | |
dc.contributor.author | Sanz Muñoz, David | |
dc.date.accessioned | 2024-02-06T15:42:23Z | |
dc.date.available | 2024-02-06T15:42:23Z | |
dc.date.issued | 2021-08-09 | |
dc.identifier.citation | Fernández-Barrero, D., Fontenla-Romero, O., Lamas-López, F., Novoa-Paradela, D., R-Moreno, M. D., & Sanz, D. (2021). SOPRENE: Assessment of the Spanish Armada’s Predictive Maintenance Tool for Naval Assets. Applied Sciences, 11, 7322. https://doi.org/10.3390/app11167322 | es |
dc.identifier.issn | 20763417 | |
dc.identifier.uri | http://hdl.handle.net/20.500.12020/1289 | |
dc.description.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. | es |
dc.description.sponsorship | MINISDEF | es |
dc.description.sponsorship | DGAM-PLATIN | es |
dc.language.iso | en | es |
dc.publisher | MDPI | es |
dc.title | SOPRENE: Assessment of the Spanish Armada’s Predictive Maintenance Tool for Naval Assets | es |
dc.type | article | es |
dc.identifier.doi | https://doi.org/10.3390/app11167322 | |
dc.issue.number | 16 | es |
dc.journal.title | Applied Sciences | es |
dc.page.initial | 7322 | es |
dc.page.final | 7340 | es |
dc.relation.projectID | Spanish Ministry of Defence) with project funding number ‘2018/1003218001400 | es |
dc.rights.accessRights | openAccess | es |
dc.subject.area | Ingenierías | es |
dc.subject.area | Matemáticas y Física | es |
dc.subject.keyword | predictive maintenance | es |
dc.subject.keyword | behavioural anomaly detection | es |
dc.subject.keyword | machine learning | es |
dc.subject.keyword | deep learning | es |
dc.subject.keyword | warships | es |
dc.subject.unesco | 3310.04 Ingeniería de Mantenimiento | es |
dc.subject.unesco | 1209.03 Análisis de Datos | es |
dc.volume.number | 11 | es |