| Autor: |
Manikar, Sagar Shenoy, Jézégou, Joël, Saqui-Sannes, Pierre de, Asseman, Philippe, Bénard, Emmanuel |
| Přispěvatelé: |
Airbus (FRANCE), Institut Supérieur de l'Aéronautique et de l'Espace - ISAE-SUPAERO (FRANCE) |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Popis: |
Aircraft operational performance is a key factor to achieve airline profitability and meet passenger expectations. It is determined by the ‘operability’ of major aircraft components along with the operational context in which the aircraft operates. Operability is the ability of a system to meet its operational requirements in terms of reliability, availability and costs. This paper proposes a approach to take into account the type of technology employed in a major aircraft component to perform operability projections. An operability model is developed using Bayesian networks that helps project the influence of different input parameters on the operational performance of the major aircraft components. An approach combining engineering and in-service data is used to instantiate the different parameters and train the Bayesian network model. The trained model can be used by system designers to perform operability projections of different design solutions through Bayesian inference and make trade-off studies from an operability point of view. Clustering of the data using unsupervised learning is also addressed in this paper to identify the best combinations of input parameters that can produce the desirable operational performance. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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