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SVETLANA

Financiado

Cerrado

Safety and maintenance improVEment Through automated fLight data ANAlysis

The objective of SVETLANA project is to involve important EU and Russian Federation industries and research organisations from the aviation area into a common ambitious effort to enhance flight operational safety and initiate smar... ver más

31/12/2012

SAFRAN ELECTRONICS...

4M€

Presupuesto del proyecto: 4M€

Líder del proyecto

SAFRAN ELECTRONICS DEFENSE No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Ver 5 Participantes

Financiación concedida El organismo FP7 notifico la concesión del proyecto el día 2012-12-31 No tenemos la información de la convocatoria

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Este proyecto no cuenta con búsquedas de partenariado abiertas en este momento.

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5 Participantes

SAFRAN ELECTRONICS DEFENSE

0.00€ | Lider

AYMING

N.D. | Participante

Royal Netherlands Aerospace...

N.D. | Participante

Lancaster University

N.D. | Participante

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Líder del proyecto

SAFRAN ELECTRONICS DEFENSE No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 4M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto The objective of SVETLANA project is to involve important EU and Russian Federation industries and research organisations from the aviation area into a common ambitious effort to enhance flight operational safety and initiate smart maintenance through intelligent and automated Flight Data Monitoring (FDM). More specifically, SVETLANA aims to provide an advanced smart update of flight safety by: • Deploying an automated and standardised flight data management cycle that is capable of processing routinely large amounts of data to allow operators to examine all data from every flight deeply using advanced sophisticated algorithms; • Propose a common standard methodology for flight data analysis using singular points automation based on data from various systems and operators to be combined and processed by advanced algorithms. Analysis on a larger statistical footprint with data from various aircraft types or operators would become comparable and could be combined; • Identifying, detecting and correcting potentially unsafe trends before they manifest themselves in an incident using self-learning adapted methodologies; • Performing a study of potential parameters or assessment techniques that can help in prediction of the condition or failure of hardware on the basis of current FDR data and can reduce the amount of corrective maintenance and provide better insight for predictive maintenance cycles; • Limit the need for specialist involvement in the FDM cycle only to the decision making process, • Providing insight into abnormal events in order to adjust training, maintenance and procedures to prevent re-occurrence; • Establishing isolation criteria templates for well known conditions and situations from large simulated datasets, allowing a broader and earlier identification of abnormal conditions: • Validate and assess the advanced FDM Methodology in a simulated/synthetic environment; • Provide a common standardised solution that has acceptance in both the EU and the Russian Federation

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