TY - GEN
T1 - A new approach for probability of failure analysis with distributed failure regions
AU - Wang, Pingfeng
AU - Cui, Xiaolong
N1 - Publisher Copyright:
© 2016, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.
PY - 2016
Y1 - 2016
N2 - Failure of practical engineering systems could be induced by several correlated failure modes, and consequently reliability analysis are conducted with multiple distributed failure regions in the system random input space. Problems with distributed failure regions create a great challenge for existing reliability analysis approaches due to the discontinuity of the system performance function between these regions. This paper presents a new enhanced Monte Carlo simulation (EMCS) approach for reliability analysis and design considering distributed failure regions. The ordinary Kriging method is adopted to construct surrogate model for the performance function so that Monte Carlo simulation (MCS) can be used to estimate the reliability. A maximum failure potential based sampling scheme is developed to iteratively search failure samples and update the Kriging model. Two case studies are used to demonstrate the efficacy of the proposed methodology.
AB - Failure of practical engineering systems could be induced by several correlated failure modes, and consequently reliability analysis are conducted with multiple distributed failure regions in the system random input space. Problems with distributed failure regions create a great challenge for existing reliability analysis approaches due to the discontinuity of the system performance function between these regions. This paper presents a new enhanced Monte Carlo simulation (EMCS) approach for reliability analysis and design considering distributed failure regions. The ordinary Kriging method is adopted to construct surrogate model for the performance function so that Monte Carlo simulation (MCS) can be used to estimate the reliability. A maximum failure potential based sampling scheme is developed to iteratively search failure samples and update the Kriging model. Two case studies are used to demonstrate the efficacy of the proposed methodology.
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M3 - Conference contribution
AN - SCOPUS:84964849518
SN - 9781624103971
T3 - 18th AIAA Non-Deterministic Approaches Conference
BT - 18th AIAA Non-Deterministic Approaches Conference
PB - American Institute of Aeronautics and Astronautics Inc, AIAA
T2 - 18th AIAA Non-Deterministic Approaches Conference, 2016
Y2 - 4 January 2016 through 8 January 2016
ER -