Personalized learning algorithm for diagnosis: Application to aircraft
| Autor: | Pierre Bect, Zineb SIMEU-ABAZI, Pierre-Loïc Maisonneuve, Marc Pero, Bruno Demerliac |
|---|---|
| Přispěvatelé: | Simeu-Abazi, Zineb, Maneesh Singh, Raj B.K.N. Rao, J.P. Liyanage, Gestion et Conduite des Systèmes de Production (G-SCOP_GCSP), Laboratoire des sciences pour la conception, l'optimisation et la production (G-SCOP), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Eurocopter France, EADS - European Aeronautic Defense and Space, Maneesh Singh, Raj B.K.N. Rao, J.P. Liyanage |
| Jazyk: | angličtina |
| Rok vydání: | 2011 |
| Předmět: | |
| Zdroj: | HAL 24th International Conference on Condition Monitoring And Diagnostic Engineering Management (COMADEM2011) 24th International Conference on Condition Monitoring And Diagnostic Engineering Management (COMADEM2011), May 2011, Stavanger, Norway. pp.1352-1359 |
| Popis: | ISBN : 0-9541307-2-3; International audience; The diagnosis of avionic equipments on aircrafts is based on recorded messages during the flight. These messages are downloaded on a ground station which is able to analyze them and provide a diagnosis to the specialized maintenance team. For a diagnosis establishment, a correlation between all messages is performed, thanks to specific rules. These rules are currently defined by an expert who uses his knowledge of the system, the avionics architecture and the feedback from customers. These rules are only based on the messages recorded during the flight. They are able to use specific characteristics of one failure code or of a grouping of failure code but they cannot take into account the environmental information. As a result, this implies the construction of more complex rules and more adapted to the system environment. This paper introduces our personalized learning process based on two learning loops. A new automatic learning algorithm uses customer feedback and takes into consideration contextual parameters relative to system's environment is proposed. This algorithm allows to design specific rules for every aircraft functioning modes. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|