Importance Sampling and Sensitivity Analysis for Reliability Assessment of Hybrid Dynamic Systems Represented by Piecewise Deterministic Markov Processes

2021 ◽  
Author(s):  
G. Chennetier ◽  
H. Chraibi ◽  
A. Dutfoy ◽  
J. Garnier
Keyword(s):  
Sensitivity Analysis ◽  
Importance Sampling ◽  
Markov Processes ◽  
Dynamic Systems ◽  
Reliability Assessment ◽  
Piecewise Deterministic Markov Processes

scholarly journals Variance based sensitivity analysis for Monte Carlo and importance sampling reliability assessment with Gaussian processes

2021 ◽  
Vol 93 ◽  
pp. 102116
Author(s):  
Morgane Menz ◽  
Sylvain Dubreuil ◽  
Jérôme Morio ◽  
Christian Gogu ◽  
Nathalie Bartoli ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Importance Sampling ◽  
Gaussian Processes ◽  
Reliability Assessment

scholarly journals Drone Ground Impact Footprints with Importance Sampling: Estimation and Sensitivity Analysis

2021 ◽  
Vol 11 (9) ◽  
pp. 3871
Author(s):  
Jérôme Morio ◽  
Baptiste Levasseur ◽  
Sylvain Bertrand
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Importance Sampling ◽  
Loss Of Control ◽  
Minimum Volume ◽  
Multiple Importance Sampling ◽  
Impact Position ◽  
Engine Failure ◽  
Main Engine ◽  
Ground Impact

This paper addresses the estimation of accurate extreme ground impact footprints and probabilistic maps due to a total loss of control of fixed-wing unmanned aerial vehicles after a main engine failure. In this paper, we focus on the ground impact footprints that contains 95%, 99% and 99.9% of the drone impacts. These regions are defined here with density minimum volume sets and may be estimated by Monte Carlo methods. As Monte Carlo approaches lead to an underestimation of extreme ground impact footprints, we consider in this article multiple importance sampling to evaluate them. Then, we perform a reliability oriented sensitivity analysis, to estimate the most influential uncertain parameters on the ground impact position. We show the results of these estimations on a realistic drone flight scenario.

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