基于未损伤结构数据的检测曲线预测概率

IF 5.7 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY Structural Health Monitoring-An International Journal Pub Date : 2023-09-08 DOI:10.1177/14759217231193088
A. Mendler, Michael Döhler, Christian U. Grosse
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引用次数: 0

摘要

本文提出了一种模型辅助方法来确定检测曲线的预测概率。该方法是“模型辅助”的,因为损伤敏感特征是结合所检查结构的数值模型进行评估的。它是“预测性的”,因为可以根据未损坏结构的测量记录构建检测概率(POD)曲线,避免任何破坏性测试。只要特征的统计分布可以用正态分布近似,该方法可以应用于结构健康监测和无损检测中的各种损伤敏感特征。特别是,它适用于基于全局振动的特征,如模态参数,并评估局部结构部件的变化,例如材料特性、横截面值、预应力和支撑条件的变化。该方法明确考虑了由于测量噪声、未知激励或其他噪声源引起的特征的统计不确定性。此外,通过置信区间,它考虑了由于结构参数不确定以及建模结构和实际结构之间可能不匹配而导致的基于模型的不确定性。基于实验室梁结构的实验研究表明,该方法可以在损伤发生前预测POD。最后,讨论了利用预测POD曲线的几种方法,例如,用于评估最合适的测量设备、用于质量控制、用于特征选择或传感器布置优化。
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Predictive probability of detection curves based on data from undamaged structures
This paper develops a model-assisted approach for determining predictive probability of detection curves. The approach is “model-assisted,” as the damage-sensitive features are evaluated in combination with a numerical model of the examined structure. It is “predictive” in the sense that probability of detection (POD) curves can be constructed based on measurement records from the undamaged structure, avoiding any destructive tests. The approach can be applied to a wide range of damage-sensitive features in structural health monitoring and non-destructive testing, provided the statistical distribution of the features can be approximated by a normal distribution. In particular, it is suitable for global vibration-based features, such as modal parameters, and evaluates changes in local structural components, for example, changes in material properties, cross-sectional values, prestressing forces, and support conditions. The approach explicitly considers the statistical uncertainties of the features due to measurement noise, unknown excitation, or other noise sources. Moreover, through confidence intervals, it considers model-based uncertainties due to uncertain structural parameters and a possible mismatch between the modeled and the real structure. Experimental studies based on a laboratory beam structure demonstrate that the approach can predict the POD before damage occurs. Ultimately, several ways to utilize predictive POD curves are discussed, for example, for the evaluation of the most suitable measurement equipment, for quality control, for feature selection, or sensor placement optimization.
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来源期刊
CiteScore
12.80
自引率
12.10%
发文量
181
审稿时长
4.8 months
期刊介绍: Structural Health Monitoring is an international peer reviewed journal that publishes the highest quality original research that contain theoretical, analytical, and experimental investigations that advance the body of knowledge and its application in the discipline of structural health monitoring.
期刊最新文献
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