Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 8th International Symposium on Reliability Engineering and Risk Management, ISRERM 2022 |
| Editors | Michael Beer, Enrico Zio, Kok-Kwang Phoon, Bilal M. Ayyub |
| Pages | 40-46 |
| Number of pages | 7 |
| Publication status | Published - 2024 |
| Event | 8th International Symposium on Reliability Engineering and Risk Management, ISRERM 2022 - Hannover, Germany Duration: 4 Sept 2022 → 7 Sept 2022 |
Abstract
First-passage probability estimation of stochastic dynamic systems is an important but still challenging problem in various science and engineering fields. This paper proposes a novel parametric approach, termed ‘fractional moments-based mixture distribution’ (FMs-MD), to address this challenge. Such method is based on capturing the extreme value distribution (EVD) of the studied stochastic system response in the first place. The concept of FM is then introduced to characterize the EVD, which is by definition a multi- (high-) dimensional integration. To efficiently evaluate the FM, a parallel adaptive strategy is developed by applying a sequential sampling technique, namely, refined Latinized stratified sampling (RLSS). By taking advantage of RLSS, both variance-reduction and parallel computing are possible in the process of FM computation. From the knowledge of low-order FMs, the EVD is then intended to be reconstructed. One flexible MD model is proposed on the basis of the extended Lognormal and generalized inverse Gaussian distributions. By fitting a set of FMs, the EVD can be reconstructed via this mixture model. The performance of the proposed method is verified by a numerical example consisting of a Duffing oscillator with random parameters under Gaussian white noise.
Keywords
- extreme value distribution, first-passage probability, fractional moments, mixture distribution, stochastic dynamic systems
ASJC Scopus subject areas
- Decision Sciences(all)
- Management Science and Operations Research
- Engineering(all)
- Safety, Risk, Reliability and Quality
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Proceedings of the 8th International Symposium on Reliability Engineering and Risk Management, ISRERM 2022. ed. / Michael Beer; Enrico Zio; Kok-Kwang Phoon; Bilal M. Ayyub. 2024. p. 40-46.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - First-passage probability estimation of stochastic dynamic systems by a parametric approach
AU - Ding, Chen
AU - Dang, Chao
AU - Broggi, Matteo
AU - Beer, Michael
N1 - Publisher Copyright: © 2022 ISRERM Organizers. Published by Research Publishing, Singapore.
PY - 2024
Y1 - 2024
N2 - First-passage probability estimation of stochastic dynamic systems is an important but still challenging problem in various science and engineering fields. This paper proposes a novel parametric approach, termed ‘fractional moments-based mixture distribution’ (FMs-MD), to address this challenge. Such method is based on capturing the extreme value distribution (EVD) of the studied stochastic system response in the first place. The concept of FM is then introduced to characterize the EVD, which is by definition a multi- (high-) dimensional integration. To efficiently evaluate the FM, a parallel adaptive strategy is developed by applying a sequential sampling technique, namely, refined Latinized stratified sampling (RLSS). By taking advantage of RLSS, both variance-reduction and parallel computing are possible in the process of FM computation. From the knowledge of low-order FMs, the EVD is then intended to be reconstructed. One flexible MD model is proposed on the basis of the extended Lognormal and generalized inverse Gaussian distributions. By fitting a set of FMs, the EVD can be reconstructed via this mixture model. The performance of the proposed method is verified by a numerical example consisting of a Duffing oscillator with random parameters under Gaussian white noise.
AB - First-passage probability estimation of stochastic dynamic systems is an important but still challenging problem in various science and engineering fields. This paper proposes a novel parametric approach, termed ‘fractional moments-based mixture distribution’ (FMs-MD), to address this challenge. Such method is based on capturing the extreme value distribution (EVD) of the studied stochastic system response in the first place. The concept of FM is then introduced to characterize the EVD, which is by definition a multi- (high-) dimensional integration. To efficiently evaluate the FM, a parallel adaptive strategy is developed by applying a sequential sampling technique, namely, refined Latinized stratified sampling (RLSS). By taking advantage of RLSS, both variance-reduction and parallel computing are possible in the process of FM computation. From the knowledge of low-order FMs, the EVD is then intended to be reconstructed. One flexible MD model is proposed on the basis of the extended Lognormal and generalized inverse Gaussian distributions. By fitting a set of FMs, the EVD can be reconstructed via this mixture model. The performance of the proposed method is verified by a numerical example consisting of a Duffing oscillator with random parameters under Gaussian white noise.
KW - extreme value distribution
KW - first-passage probability
KW - fractional moments
KW - mixture distribution
KW - stochastic dynamic systems
UR - http://www.scopus.com/inward/record.url?scp=85202015815&partnerID=8YFLogxK
U2 - 10.3850/978-981-18-5184-1_MS-01-175-cd
DO - 10.3850/978-981-18-5184-1_MS-01-175-cd
M3 - Conference contribution
AN - SCOPUS:85202015815
SN - 9789811851841
SP - 40
EP - 46
BT - Proceedings of the 8th International Symposium on Reliability Engineering and Risk Management, ISRERM 2022
A2 - Beer, Michael
A2 - Zio, Enrico
A2 - Phoon, Kok-Kwang
A2 - Ayyub, Bilal M.
T2 - 8th International Symposium on Reliability Engineering and Risk Management, ISRERM 2022
Y2 - 4 September 2022 through 7 September 2022
ER -