A NOVEL ONLINE STRUCTURE DAMAGE IDENTIFICATION USING PRINCIPAL COMPONENT ANALYSIS (PCA)

机译：使用主成分分析（PCA）的新型在线结构损坏识别

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A novel online structure damage identification using Principal Component Analysis (PCA) techniques and the perceptron backpropagation neural network is presented.There are three phases to execute this method. In Phase I, system modal information, frequencies and mode shapes, are calculated. Phase II is for damage location identification; the Residual Force Vectors (RFVs) are computed as input to the first neural network. Then the network was trained to simulate damage location identification. Phase III is the severity identification step. The PCA method is used to modify the input for the second neural network. Then this network identifies the severity. There are three advantages of using the PCA method. First, PCA method characterizes the original modal information precisely. Second, PCA method creates the unique data for different damage cases unlike other modal property based data. Third, the accuracy of the damage identification improves significantly, when compared with previously developed methods. This method can be operated online for the real time structural damage identification.

机译：介绍了使用主成分分析（PCA）技术和Perceptron BackProjagation神经网络的新型在线结构损坏识别。执行此方法是三个阶段。在阶段I中，计算系统模态信息，频率和模式形状。第二阶段是损坏位置识别;将残余力矢量（RFV）计算为对第一神经网络的输入。然后，网络训练以模拟损坏位置识别。阶段III是严重程度识别步骤。 PCA方法用于修改第二神经网络的输入。然后该网络标识严重性。使用PCA方法有三种优点。首先，PCA方法精确地表征了原始模态信息。其次，与其他基于模态属性的数据不同，PCA方法为不同的损坏情况创建唯一数据。第三，与先前开发的方法相比，损坏识别的准确性显着提高。该方法可以在线运行，以便实时结构损坏识别。

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《American Society of Mechanical Engineers Power Conference》|2007年||共8页
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Soonyoung Hong; M.-H. Herman Shen;
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A NOVEL ONLINE STRUCTURE DAMAGE IDENTIFICATION USING PRINCIPAL COMPONENT ANALYSIS (PCA)

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