TY - GEN
T1 - IMPORTANCE SAMPLING AND SENSITIVITY ANALYSIS FOR RELIABILITY ASSESSMENT OF HYBRID DYNAMIC SYSTEMS REPRESENTED BY PIECEWISE DETERMINISTIC MARKOV PROCESSES
AU - Chennetier, G.
AU - Chraibi, H.
AU - Dutfoy, A.
AU - Garnier, J.
N1 - Publisher Copyright:
© ESREL 2021. Published by Research Publishing, Singapore.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - A natural way to assess the reliability of a complex industrial system is to carry out numerical simulations that reproduce the behavior of the system. The PyCATSHOO1 tool developed by Électricité De France (EDF R&D) allows the modeling of such systems through the framework of piecewise deterministic Markov processes (PDMP). These processes have a discrete stochastic behavior (failures, reconfigurations, control mechanisms, repairs, etc.) in interaction with continuous deterministic physical phenomena. It is well known that for sufficiently rare events, crude Monte-Carlo methods require a very large number of simulations to accurately estimate their probability of occurrence. We propose an adaptive importance sampling strategy based on a Cross-Entropy method to reduce the cost of estimating the probability of system failure2 . The success of this method depends crucially on the family of instrumental laws used to approximate the optimal law. We construct this family according to the PDMP structure of the system, in particular according to the configuration of its minimal failure groups. Finally, we propose different sensitivity analysis techniques3 to reduce the dimension of the problem and to determine the respective contributions of different component failure modes to the probability of system mission loss. We present an application of this strategy on a test case from the nuclear industry: the spent fuel pool.
AB - A natural way to assess the reliability of a complex industrial system is to carry out numerical simulations that reproduce the behavior of the system. The PyCATSHOO1 tool developed by Électricité De France (EDF R&D) allows the modeling of such systems through the framework of piecewise deterministic Markov processes (PDMP). These processes have a discrete stochastic behavior (failures, reconfigurations, control mechanisms, repairs, etc.) in interaction with continuous deterministic physical phenomena. It is well known that for sufficiently rare events, crude Monte-Carlo methods require a very large number of simulations to accurately estimate their probability of occurrence. We propose an adaptive importance sampling strategy based on a Cross-Entropy method to reduce the cost of estimating the probability of system failure2 . The success of this method depends crucially on the family of instrumental laws used to approximate the optimal law. We construct this family according to the PDMP structure of the system, in particular according to the configuration of its minimal failure groups. Finally, we propose different sensitivity analysis techniques3 to reduce the dimension of the problem and to determine the respective contributions of different component failure modes to the probability of system mission loss. We present an application of this strategy on a test case from the nuclear industry: the spent fuel pool.
KW - Cross Entropy
KW - Dimension reduction
KW - Hybrid dynamic systems
KW - Importance sampling
KW - Piecewise deterministic Markov processes
KW - Rare event simulation
KW - Sensitivity analysis
UR - https://www.scopus.com/pages/publications/85135448317
U2 - 10.3850/978-981-18-2016-8_736-cd
DO - 10.3850/978-981-18-2016-8_736-cd
M3 - Conference contribution
AN - SCOPUS:85135448317
SN - 9789811820168
T3 - Proceedings of the 31st European Safety and Reliability Conference, ESREL 2021
SP - 3274
BT - Proceedings of the 31st European Safety and Reliability Conference, ESREL 2021
A2 - Castanier, Bruno
A2 - Cepin, Marko
A2 - Bigaud, David
A2 - Berenguer, Christophe
PB - Research Publishing, Singapore
T2 - 31st European Safety and Reliability Conference, ESREL 2021
Y2 - 19 September 2021 through 23 September 2021
ER -