Aircraft Numerical "Twin": A Time Series Regression Competition

Adrien Pavao, Isabelle Guyon, Nachar Stephane, Fabrice Lebeau, Martin Ghienne, Ludovic Platon, Tristan Barbagelata, Pierre Escamilla, Sana Mzali, Meng Liao, Sylvain Lassonde, Antonin Braun (+19 others)
2021 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)  
This paper presents the design and analysis of a data science competition on a problem of time series regression from aeronautics data. For the purpose of performing predictive maintenance, aviation companies seek to create aircraft "numerical twins", which are programs capable of accurately predicting strains at strategic positions in various body parts of the aircraft. Given a number of input parameters (sensor data) recorded in sequence during the flight, the competition participants had to
more » ... redict output values (gauges), also recorded sequentially during test flights, but not recorded during regular flights. The competition data included hundreds of complete flights. It was a code submission competition with complete blind testing of algorithms. The results indicate that such a problem can be effectively solved with gradient boosted trees, after preprocessing and feature engineering. Deep learning methods did not prove as efficient.
doi:10.1109/icmla52953.2021.00075 fatcat:ums54eyqqbajla266s7qlfp34a