Failure probability estimation of dynamic systems employing relaxed power spectral density functions with dependent frequency modeling and sampling

verfasst von: Marco Behrendt, Meng Ze Lyu, Yi Luo, Jian Bing Chen, Michael Beer
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.
Organisationseinheit(en): Institut für Risiko und Zuverlässigkeit
Externe Organisation(en): Tongji University
The University of Liverpool
Typ: Artikel
Journal: Probabilistic Engineering Mechanics
Band: 75
Anzahl der Seiten: 10
ISSN: 0266-8920
Publikationsdatum: 01.2024
Publikationsstatus: Veröffentlicht
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Statistische und nichtlineare Physik, Tief- und Ingenieurbau, Kernenergie und Kernkraftwerkstechnik, Physik der kondensierten Materie, Luft- und Raumfahrttechnik, Meerestechnik, Maschinenbau
Elektronische Version(en): https://doi.org/10.1016/j.probengmech.2024.103592 (Zugang: Geschlossen)

BibTeX

@article{9a0ce68bcc5f47cabfadcb42c4f503d5,
title = "Failure probability estimation of dynamic systems employing relaxed power spectral density functions with dependent frequency modeling and sampling",
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.",
keywords = "Power spectral density function, Probabilistic dependency, Stochastic dynamics, Stochastic processes, Uncertainty quantification",
author = "Marco Behrendt and Lyu, {Meng Ze} and Yi Luo and Chen, {Jian Bing} and Michael Beer",
note = "Funding Information: This work was supported by the National Natural Science Foundation of China (Grant No. 12302037); the China Postdoctoral Science Foundation, China (Grant No. 2023M732669); the Shanghai Post-Doctoral Excellence Program, China (Grant No. 2022558); and the European Union's Horizon 2020 research and innovation programme under Marie Sklodowska-Curie project GREYDIENT – Grant Agreement n°955393.",
year = "2024",
month = jan,
doi = "10.1016/j.probengmech.2024.103592",
language = "English",
volume = "75",
journal = "Probabilistic Engineering Mechanics",
issn = "0266-8920",
publisher = "Elsevier Ltd.",
}