TY - JOUR

T1 - Failure probability estimation of dynamic systems employing relaxed power spectral density functions with dependent frequency modeling and sampling

AU - Behrendt, Marco

AU - Lyu, Meng Ze

AU - Luo, Yi

AU - Chen, Jian Bing

AU - Beer, Michael

N1 - 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.

PY - 2024/1

Y1 - 2024/1

N2 - 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.

AB - 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.

KW - Power spectral density function

KW - Probabilistic dependency

KW - Stochastic dynamics

KW - Stochastic processes

KW - Uncertainty quantification

UR - http://www.scopus.com/inward/record.url?scp=85186193755&partnerID=8YFLogxK

U2 - 10.1016/j.probengmech.2024.103592

DO - 10.1016/j.probengmech.2024.103592

M3 - Article

AN - SCOPUS:85186193755

VL - 75

JO - Probabilistic Engineering Mechanics

JF - Probabilistic Engineering Mechanics

SN - 0266-8920

M1 - 103592

ER -