Kernel methods for flight data monitoring
Nicolas Chrysanthos · HAL (Le Centre pour la Communication Scientifique Directe) · 2014
Flight Data Monitoring (FDM), is the process by which an airline routinely collects, processes, and analyses the data recorded in aircrafts with the goal of improving the overall safety or operational efficiency.The goal of this thesis is to investigate machine learning methods, and in particular kernel methods, for the detection of atypical flights that may present problems that cannot be found using traditional methods.Atypical flights may present safety of operational issues and thus need to be studied by an FDM expert.In the first part we propose a novel method for anomaly detection that is suited to the constraints of the field of FDM.We rely on a novel dimensionality reduction technique called kernel entropy component analysis to design a method which is both unsupervised and robust.In the second part we solve the most salient issue regarding the field of FDM, which is how the data is structured.Firstly, we extend the method to take into account parameters of diverse types such as continuous, discrete or angular.Secondly, we explore techniques to take into account the temporal aspect of flights and propose a new kernel in the family of dynamic time warping techniques, and demonstrate that it is faster to compute than competing techniques and is positive definite.We illustrate our approach with promising results on real world datasets from airlines TAP and Transavia comprising hundreds of flights