Behrendt, Marco, Lyu, Meng-Ze, Luo, Yi, Chen, Jian-Bing and Beer, Michael 
ORCID: 0000-0002-0611-0345
(2024)
Failure probability estimation of dynamic systems employing relaxed power spectral density functions with dependent frequency modeling and sampling.
Probabilistic Engineering Mechanics, 75.
p. 103592.
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Behrendt et al. - 2024 - Failure probability estimation of dynamic systems .pdf - Author Accepted Manuscript Available under License Creative Commons Attribution. Download (694kB) | Preview |
Abstract
This work addresses the critical task of accurately estimating failure probabilities in dynamic systems by utilizing a probabilistic load model based on a set of data with similar characteristics, namely the relaxed power spectral density (PSD) function. A major drawback of the relaxed PSD function is the lack of dependency between frequencies, which leads to unrealistic PSD functions being sampled, resulting in an unfavorable effect on the failure probability estimation. In this work, this limitation is addressed by various methods of modeling the dependency, including the incorporation of statistical quantities such as the correlation present in the data set. Specifically, a novel technique is proposed, incorporating probabilistic dependencies between different frequencies for sampling representative PSD functions, thereby enhancing the realism of load representation. By accounting for the dependencies between frequencies, the relaxed PSD function enhances the precision of failure probability estimates, opening the opportunity for a more robust and accurate reliability assessment under uncertainty. The effectiveness and accuracy of the proposed approach is demonstrated through numerical examples, showcasing its ability to provide reliable failure probability estimates in dynamic systems.
| Item Type: | Article |
|---|---|
| Divisions: | Faculty of Science and Engineering > School of Engineering |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 12 Mar 2024 09:12 |
| Last Modified: | 12 Mar 2024 11:44 |
| DOI: | 10.1016/j.probengmech.2024.103592 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3179273 |
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