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Statistical methods for critical scenarios in aeronautics. (English. French summary) Zbl 1341.62320

Summary: We present numerical results obtained on the CEMRACS project Predictive SMS proposed by Safety Line. The goal of this work was to elaborate a purely statistical method in order to reconstruct the deceleration profile of a plane during landing under normal operating conditions, from a database containing around 1500 recordings. The aim of Safety Line is to use this model to detect malfunctions of the braking system of the plane from deviations of the measured deceleration profile of the plane to the one predicted by the model. This yields to a multivariate nonparametric regression problem, which we chose to tackle using a Bayesian approach based on the use of Gaussian processes similar to the one presented in [C. E. Rasmussen and C. K. I. Williams, Gaussian processes for machine learning. Cambridge, MA: MIT Press (2006; Zbl 1177.68165)]. We also compare this approach with other statistical methods.

MSC:

62P30 Applications of statistics in engineering and industry; control charts

Citations:

Zbl 1177.68165
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