{"type":"ScholarlyArticle","title":"AESim: A Data-Driven Aircraft Engine Simulator","authors":[{"name":"Abdellah Madane","slug":"abdellah-madane","given":"Abdellah","family":"Madane","affiliation":"David Lab , UVSQ , Paris-Saclay University , 78035 Versailles , France; DataLab , Safran Aircraft Engines , 77550 Moissy-Cramayel , France"},{"name":"Florent Forest","slug":"florent-forest","given":"Florent","family":"Forest","affiliation":"IMOS Lab , EPFL , Lausanne , Switzerland"},{"name":"Hanane Azzag","slug":"hanane-azzag","given":"Hanane","family":"Azzag","affiliation":"LIPN , UMR CNRS 7030 , Sorbonne Paris Nord University , 93430 Villetaneuse , France"},{"name":"Mustapha Lebbah","slug":"mustapha-lebbah","given":"Mustapha","family":"Lebbah","affiliation":"David Lab , UVSQ , Paris-Saclay University , 78035 Versailles , France"},{"name":"Jérôme Lacaille","slug":"jerome-lacaille","given":"Jérôme","family":"Lacaille","affiliation":"DataLab , Safran Aircraft Engines , 77550 Moissy-Cramayel , France"}],"abstract":"We present AESim, a data-driven Aircraft Engine Simulator developed using transformer-based conditional generative adversarial networks. AESim generates samples of aircraft engine sensor measurements over full flights, conditioned on a given flight mission profile representing the flight conditions. It constitutes an essential tool in aircraft engine digital twins, capable of simulating their performance for different flight missions. It allows for comparison of the behavior of different engines under the same operational conditions, simulation of various scenarios for a given engine, facilitating applications like engine behavior analysis, performance limit identification, and optimization of maintenance schedules within a global Prognostics and Health Management (PHM) strategy. It also allows the imputation of missing flight data and addresses confidentiality concerns by generating synthetic flight datasets that can be shared for public research purposes or data challenges.","doi":"10.24963/ijcai.2024/1021","url":"https://ijcai.org/proceedings/2024/1021","pdf":"https://ijcai.org/proceedings/2024/1021.pdf","markdown":"https://ijcai.org/proceedings/2024/1021.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/1021","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Demo Track","track":"Demo Track","number":1021,"pages":{"first":8737,"last":8740},"keywords":["Machine Learning: ML: Generative adverserial networks","Machine Learning: ML: Applications","Machine Learning: ML: Attention models","Machine Learning: ML: Time series and data streams"],"videos":[],"references":[{"position":1,"raw":"[Coussirou et al., 2022] Jean Coussirou, Thomas Vanaret, Jérôme Lacaille, and Safran Aircraft Engines DataLab. 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