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Vibration-based health monitoring approach for composite structures using multivatiate statistical analysis

机译:基于多元统计分析的复合结构基于振动的健康监测方法

摘要

In this paper a novel procedure for damage assessment is suggested which is based on singular spectrum analysis (SSA). The main feature of the method is that it applies Principal Component Analysis (PCA) to the lagged time series, obtained from the measured structural vibration response. In this study the methodology is developed for the case of a free decay response. The measured acceleration vectors are transformed into the frequency domain and then used to define a trajectory matrix. The covariance matrix of the trajectory matrix is decomposed into new variables, the Principal Components (PCs). They define a new space of linearly correlated variables onto which the dynamics/motion of the system can be projected. This decomposition is used to uncover oscillation patterns among other purposes. The method is applied and demonstrated for the case of a simple 2-DoF system. To demonstrate its capabilities for damage diagnosis different levels of stiffness reduction are introduced. The first two PCs are used to visually demonstrate the abilities of the methodology. The Mahalanobis distance is used to develop a classification system to detect and localize delamination in the 2-DoF system. The results clearly demonstrate the capabilities of the system to clearly detect and localize damage.
机译:在本文中,提出了一种基于奇异频谱分析(SSA)的损伤评估新方法。该方法的主要特征是将主成分分析(PCA)应用于从测量的结构振动响应获得的滞后时间序列。在这项研究中,针对自由衰减响应的情况开发了该方法。测得的加速度矢量被转换到频域中,然后用于定义轨迹矩阵。轨迹矩阵的协方差矩阵被分解为新变量,即主成分(PC)。它们定义了线性相关变量的新空间,系统的动态/运动可以投影到该空间上。除其他目的外,该分解还用于揭示振荡模式。该方法适用于简单的2自由度系统。为了证明其用于损伤诊断的能力,引入了不同程度的刚度降低。前两台PC用于直观地展示该方法的功能。马氏距离用于开发分类系统,以检测和定位2-DoF系统中的分层。结果清楚地表明了系统清楚地检测和定位损坏的能力。

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