First-passage probability estimation of stochastic dynamic systems by a parametric approach

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  • The University of Liverpool
  • Tongji University
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OriginalspracheEnglisch
Titel des SammelwerksProceedings of the 8th International Symposium on Reliability Engineering and Risk Management, ISRERM 2022
Herausgeber/-innenMichael Beer, Enrico Zio, Kok-Kwang Phoon, Bilal M. Ayyub
Seiten40-46
Seitenumfang7
PublikationsstatusVeröffentlicht - 2024
Veranstaltung8th International Symposium on Reliability Engineering and Risk Management, ISRERM 2022 - Hannover, Deutschland
Dauer: 4 Sept. 20227 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.

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First-passage probability estimation of stochastic dynamic systems by a parametric approach. / Ding, Chen; Dang, Chao; Broggi, Matteo et al.
Proceedings of the 8th International Symposium on Reliability Engineering and Risk Management, ISRERM 2022. Hrsg. / Michael Beer; Enrico Zio; Kok-Kwang Phoon; Bilal M. Ayyub. 2024. S. 40-46.

Publikation: Beitrag in Buch/Bericht/Sammelwerk/KonferenzbandAufsatz in KonferenzbandForschungPeer-Review

Ding, C, Dang, C, Broggi, M & Beer, M 2024, First-passage probability estimation of stochastic dynamic systems by a parametric approach. in M Beer, E Zio, K-K Phoon & BM Ayyub (Hrsg.), Proceedings of the 8th International Symposium on Reliability Engineering and Risk Management, ISRERM 2022. S. 40-46, 8th International Symposium on Reliability Engineering and Risk Management, ISRERM 2022, Hannover, Deutschland, 4 Sept. 2022. https://doi.org/10.3850/978-981-18-5184-1_MS-01-175-cd
Ding, C., Dang, C., Broggi, M., & Beer, M. (2024). First-passage probability estimation of stochastic dynamic systems by a parametric approach. In M. Beer, E. Zio, K.-K. Phoon, & B. M. Ayyub (Hrsg.), Proceedings of the 8th International Symposium on Reliability Engineering and Risk Management, ISRERM 2022 (S. 40-46) https://doi.org/10.3850/978-981-18-5184-1_MS-01-175-cd
Ding C, Dang C, Broggi M, Beer M. First-passage probability estimation of stochastic dynamic systems by a parametric approach. in Beer M, Zio E, Phoon KK, Ayyub BM, Hrsg., Proceedings of the 8th International Symposium on Reliability Engineering and Risk Management, ISRERM 2022. 2024. S. 40-46 doi: 10.3850/978-981-18-5184-1_MS-01-175-cd
Ding, Chen ; Dang, Chao ; Broggi, Matteo et al. / First-passage probability estimation of stochastic dynamic systems by a parametric approach. Proceedings of the 8th International Symposium on Reliability Engineering and Risk Management, ISRERM 2022. Hrsg. / Michael Beer ; Enrico Zio ; Kok-Kwang Phoon ; Bilal M. Ayyub. 2024. S. 40-46
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AU - Ding, Chen

AU - Dang, Chao

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