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

IF 3 3区 工程技术 Q2 ENGINEERING, MECHANICAL Probabilistic Engineering Mechanics Pub Date : 2024-01-01 DOI:10.1016/j.probengmech.2024.103592
Marco Behrendt , Meng-Ze Lyu , Yi Luo , Jian-Bing Chen , Michael Beer
{"title":"Failure probability estimation of dynamic systems employing relaxed power spectral density functions with dependent frequency modeling and sampling","authors":"Marco Behrendt ,&nbsp;Meng-Ze Lyu ,&nbsp;Yi Luo ,&nbsp;Jian-Bing Chen ,&nbsp;Michael Beer","doi":"10.1016/j.probengmech.2024.103592","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Probabilistic Engineering Mechanics","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0266892024000146","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
引用次数: 0

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.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用依频率建模和采样的松弛功率谱密度函数估算动态系统的故障概率
这项研究通过利用基于一组具有相似特征的数据(即松弛功率谱密度 (PSD) 函数)的概率负荷模型,解决了在动态系统中准确估计故障概率的关键任务。松弛 PSD 函数的一个主要缺点是频率之间缺乏相关性,这导致采样的 PSD 函数不切实际,从而对故障概率估计产生不利影响。在这项工作中,我们采用了各种方法来模拟这种依赖性,包括纳入数据集中存在的相关性等统计量,从而解决了这一局限性。具体来说,本文提出了一种新颖的技术,在对具有代表性的 PSD 函数进行采样时,将不同频率之间的概率依赖关系纳入其中,从而增强了载荷表示的真实性。通过考虑频率之间的依赖关系,放宽 PSD 函数提高了故障概率估计的精确度,为在不确定情况下进行更稳健、更准确的可靠性评估提供了机会。我们通过数值示例证明了所提方法的有效性和准确性,展示了其在动态系统中提供可靠故障概率估计的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Probabilistic Engineering Mechanics
Probabilistic Engineering Mechanics 工程技术-工程:机械
CiteScore
3.80
自引率
15.40%
发文量
98
审稿时长
13.5 months
期刊介绍: This journal provides a forum for scholarly work dealing primarily with probabilistic and statistical approaches to contemporary solid/structural and fluid mechanics problems encountered in diverse technical disciplines such as aerospace, civil, marine, mechanical, and nuclear engineering. The journal aims to maintain a healthy balance between general solution techniques and problem-specific results, encouraging a fruitful exchange of ideas among disparate engineering specialities.
期刊最新文献
Real-time anomaly detection of the stochastically excited systems on spherical (S2) manifold Nonprobabilistic time-dependent reliability analysis for uncertain structures under interval process loads Fractional-order filter approximations for efficient stochastic response determination of wind-excited linear structural systems Seismic reliability analysis using Subset Simulation enhanced with an explorative adaptive conditional sampling algorithm Efficient optimization-based method for simultaneous calibration of load and resistance factors considering multiple target reliability indices
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1