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Bayesian based structural health management and reliability analysis techniques utilizing support vector machine.

机译：基于贝叶斯的结构健康管理和可靠性分析技术，利用支持向量机。

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摘要

Structural health and safety play a major role in all facets of human daily lives. Over the past few decades significant advancements have been made in structural damage detection and health management in a wide range of engineering disciplines and practices, including but not limited to aerospace, power generating plants, infrastructure systems, and manufacturing. Two main thrust areas of research in this field include the development of methodologies/algorithms for detection of damage and/or changes in the dynamic characteristics of the system, and the sensing/detection devices for capturing the required data/information. A third and evolving area is the integration of these two thrusts and the development of integrated systems that can "manage", and "adapt" in an "intelligent" sense, the subsequent actions that need to take place in order to maintain the integrity of the system subject to the external environment and/or loading conditions. However, majority of the developed techniques fail to take into account the important effects of uncertainty presented in sensing, system modeling, and material behavior associated with dynamic systems. These uncertainties could greatly affect the structural performance and health management, which leads to challenging issues such as reliability and life prediction of the structure. In order to address these important factors, the application of the probabilistic and reliability analysis techniques to structural health management has emerged as an active research area in recent years.; Bayesian probabilistic analysis is such a technique in which the uncertainties could be related with a mathematical model---probability distribution function---which interprets the measurement of confidence interval. The posterior probability distribution is known as an expression for the statistical knowledge of a system after a set of measurements is made. The Bayesian approach is a powerful way to continuously optimize the "posterior" probably density function (pdf) by adapting the predefined "priori" pdf based on "new" measurements. On the basis of Bayesian analysis, it is shown to be possible to perform statistical based system identification, structural damage detection and reliability assessment, as part of structural health management.; In the first stage of this thesis work, a Bayesian based system identification approach was developed to identify system parameters provided so that inherent uncertainties and probabilities of system changes and/or environmental disturbances are taken into account. It is obvious that the nature of changes encountered the system models is critical to monitoring and managing the integrity of the structural systems. In this part of the work, system changes were modeled as random variables with certain statistical properties. The effects of priori definition and different data sampling techniques were studied. To explore the application of this Bayesian based system identification approach to structural health management, the probability density function (pdf) profiles of model parameters were studied to quantify the uncertainties associated with the estimated parameters. By analyzing the posterior pdf inference, the reliability parameters of interest could also be obtained from the available data.; Structural health monitoring, damage detection and structural reliability are usually considered as the sequential components in a structural health management chain. It is the ultimate goal of structural health management to achieve a significant improvement of the structural reliability. Therefore, the second stage of this thesis work was devoted to the study of system reliability. A reliability analysis package developed in MATLAB-PROBES was enhanced with its functionality in this work. The enhancements include the new capabilities of performing analysis to correlated, non-normally distributed random variables and the added functionality to obtain the statistical informati
机译：结构健康与安全在人类日常生活的各个方面都发挥着重要作用。在过去的几十年中，结构损伤检测和健康管理在许多工程学科和实践中取得了显着进步，包括但不限于航空航天，发电厂，基础设施系统和制造。该领域的两个主要研究领域包括开发用于检测系统的动态特性的损坏和/或变化的方法/算法，以及用于捕获所需数据/信息的传感/检测设备。第三和不断发展的领域是这两个主旨的融合，以及可以“智能”意义上的“管理”和“适应”的集成系统的发展，为了保持完整性，需要采取的后续行动。系统受外部环境和/或负载条件的影响。但是，大多数已开发的技术都没有考虑到不确定性对传感，系统建模以及与动态系统相关的材料行为所产生的重要影响。这些不确定性可能会极大地影响结构性能和健康管理，从而导致具有挑战性的问题，例如结构的可靠性和寿命预测。为了解决这些重要因素，近年来，概率和可靠性分析技术在结构健康管理中的应用已成为活跃的研究领域。贝叶斯概率分析是一种将不确定性与数学模型（概率分布函数）相关的技术，该模型解释置信区间的度量。后验概率分布称为进行一组测量后的系统统计知识的表达式。贝叶斯方法是一种强大的方法，可以通过基于“新”测量结果来适应预定义的“先验” pdf，从而连续优化“后验”可能的密度函数（pdf）。基于贝叶斯分析，作为结构健康管理的一部分，可以进行基于统计的系统识别，结构损伤检测和可靠性评估。在本文工作的第一阶段，开发了一种基于贝叶斯的系统识别方法来识别所提供的系统参数，从而考虑到系统变化和/或环境干扰的固有不确定性和可能性。显然，系统模型遇到的更改的性质对于监视和管理结构系统的完整性至关重要。在这部分工作中，系统更改被建模为具有某些统计属性的随机变量。研究了先验定义和不同数据采样技术的影响。为了探索这种基于贝叶斯的系统识别方法在结构健康管理中的应用，研究了模型参数的概率密度函数（pdf）配置文件以量化与估计参数相关的不确定性。通过分析后pdf推断，还可以从可用数据中获得感兴趣的可靠性参数。结构健康监控，损伤检测和结构可靠性通常被视为结构健康管理链中的顺序组件。结构健康管理的最终目标是显着提高结构的可靠性。因此，本论文的第二阶段致力于系统可靠性的研究。在这项工作中，通过MATLAB-PROBES开发的可靠性分析软件包得到了增强。增强功能包括对相关的，非正态分布的随机变量执行分析的新功能，以及获得统计信息的新增功能。

著录项

  • 作者

    Cao, Yingfang.;

  • 作者单位

    North Carolina State University.;

  • 授予单位 North Carolina State University.;
  • 学科 Engineering Mechanical.
  • 学位 Ph.D.
  • 年度 2007
  • 页码 106 p.
  • 总页数 106
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 机械、仪表工业;
  • 关键词

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