A Fault-Detection System Approach for the Optimization of Warship Equipment Replacement Parts Based on Operation Parameters.

fault detection machine learning one-class warship

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
23 Mar 2023
Historique:
received: 27 01 2023
revised: 11 03 2023
accepted: 20 03 2023
medline: 14 4 2023
entrez: 13 4 2023
pubmed: 14 4 2023
Statut: epublish

Résumé

Systems engineering plays a key role in the naval sector, focusing on how to design, integrate, and manage complex systems throughout their life cycle; it is therefore difficult to conceive functional warships without it. To this end, specialized information systems for logistical support and the sustainability of material solutions are essential to ensure proper provisioning and to know the operational status of the frigate. However, based on an architecture composed of a set of logistics applications, this information system may require highly qualified operators with a deep knowledge of the behavior of onboard systems to manage it properly. In this regard, failure detection systems have been postulated as one of the main cutting-edge methods to address the challenge, employing intelligent techniques for observing anomalies in the normal behavior of systems without the need for expert knowledge. In this paper, the study is concerned to the scope of the Spanish navy, where a complex information system structure is responsible for ensuring the correct maintenance and provisioning of the vessels. In such context, we hereby suggest a comparison between different one-class techniques, such as statistical models, geometric boundaries, or dimensional reduction to face anomaly detection in specific subsystems of a warship, with the prospect of applying it to the whole ship.

Identifiants

pubmed: 37050448
pii: s23073389
doi: 10.3390/s23073389
pmc: PMC10099075
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Auteurs

Álvaro Michelena (Á)

Department of Industrial Engineering, University of A Coruña (UDC), CTC, CITIC, Rúa Mendizábal, s/n, 15403 Ferrol, Spain.

Víctor Caínzos López (VC)

Department of Industrial Engineering, University of A Coruña (UDC), CTC, CITIC, Rúa Mendizábal, s/n, 15403 Ferrol, Spain.

Francisco Lamas López (FL)

Centro de Supervisión y Análisis de Datos de la Armada (CESADAR), Arsenal de Cartagena, Armada Calle Real s/n, 30290 Cartagena, Spain.
Computing and Artificial Intelligence Laboratory (CAILab), Facultad de Ciencia y Tecnología, Universidad Camilo José Cela, Calle Castillo de Alarcón 49, 28692 Madrid, Spain.

Elena Arce (E)

Department of Industrial Engineering, University of A Coruña (UDC), CTC, CITIC, Rúa Mendizábal, s/n, 15403 Ferrol, Spain.

José Mendoza García (J)

Centro de Supervisión y Análisis de Datos de la Armada (CESADAR), Arsenal de Cartagena, Armada Calle Real s/n, 30290 Cartagena, Spain.
Área de Sostenimiento y Gestión Logística, ISDEFE, Calle Beatriz de Bobadilla, 3., 28040 Madrid, Spain.

Andrés Suárez-García (A)

Spanish Naval School, University Defense Center, 36920 Marín, Spain.

Guillermo García Espinosa (G)

Centro de Supervisión y Análisis de Datos de la Armada (CESADAR), Arsenal de Cartagena, Armada Calle Real s/n, 30290 Cartagena, Spain.
Área de Sostenimiento y Gestión Logística, ISDEFE, Calle Beatriz de Bobadilla, 3., 28040 Madrid, Spain.

José-Luis Calvo-Rolle (JL)

Department of Industrial Engineering, University of A Coruña (UDC), CTC, CITIC, Rúa Mendizábal, s/n, 15403 Ferrol, Spain.

Héctor Quintián (H)

Department of Industrial Engineering, University of A Coruña (UDC), CTC, CITIC, Rúa Mendizábal, s/n, 15403 Ferrol, Spain.

Classifications MeSH