Maximum likelihood estimation of probabilistically described loads in beam structures

IF 3 3区工程技术 Q2 ENGINEERING, MECHANICAL

Probabilistic Engineering Mechanics Pub Date : 2024-04-01 DOI:10.1016/j.probengmech.2024.103627

Andreas Tsiotas-Niachopetros, Nicholas E. Silionis, Konstantinos N. Anyfantis

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引用次数: 0

Abstract

In recent years, focus has been shifted towards predictive maintenance in an effort to improve the reliability of operating structures. Processing structural response data obtained from in-situ sensors during operation can provide added value towards this direction. Structural Health Monitoring (SHM) methods are uniquely suited for this task; however, accounting for the effect of stochastic structural loads is critical for their robustness. In this work, a framework based on Maximum Likelihood Estimation (MLE) is presented, whose goal is to obtain inferences on typically unobservable quantities that describe stochastic structural loading. A structural beam is employed as a demonstrative case study, that is subjected to point loads with stochastic magnitude and application points. The hyperparameters that govern their underlying probability distribution functions (pdf) are the quantities of inferential interest. The inverse (load) identification process is performed using a marginalized MLE objective, where stochastic Monte Carlo (MC) integration is employed to perform the marginalization and Genetic Algorithms (GAs) are used as the optimizer. The Cramer–Rao (CR) lower bound is used to produce 95 % Confidence Intervals (CIs) to quantify estimation uncertainty.

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梁结构中概率描述荷载的最大似然估计

近年来，人们已将重点转向预测性维护，以提高运行结构的可靠性。处理运行期间从现场传感器获得的结构响应数据可为这一方向提供附加值。结构健康监测（SHM）方法非常适合这一任务；然而，考虑随机结构载荷的影响对其稳健性至关重要。在这项工作中，提出了一个基于最大似然估计（MLE）的框架，其目标是获得描述随机结构载荷的典型不可观测量的推论。以结构梁为例进行了示范研究，该梁受到随机大小和作用点的点荷载作用。支配其基本概率分布函数（pdf）的超参数是推理中关注的量。逆（载荷）识别过程采用边际化 MLE 目标，其中随机蒙特卡罗（MC）积分用于执行边际化，遗传算法（GA）用作优化器。使用 Cramer-Rao (CR) 下限生成 95 % 置信区间 (CI)，以量化估计的不确定性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Probabilistic Engineering Mechanics 工程技术-工程：机械

CiteScore

3.80

自引率

15.40%

发文量

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.