MSPC在结构损伤辨识中的研究与应用
首发时间:2009-07-07
摘要:长期以来,结构的损伤识别都是工程界中一个活跃的研究领域,学者们提出了大量的损伤识别的方法。 本文将多元统计过程控制的理论引入到结构的损伤识别中,首先对加速度传感器得到的振动信号进行处理,分别建立时序模型(AR模型),以AR模型的系数作为损伤识别的基本参变量[1],利用多向主元分析对AR模型的系数进行压缩提炼,并以平方预测误差(SPE)值是否超限作为损伤是否存在的标志,最后讨论了主元模型残差的欧氏距离以及马氏距离在损伤识别中的应用。借助matlab软件的强大功能,对悬臂梁实验得到的振动信号数据进行分析,验证该方法的有效性。
关键词: 损伤识别 AR模型 多向主元分析(MPCA) SPE 欧氏距离 马氏距离
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The MSPC in the research and application of structural damage identification
Abstract:A long period of time,structural damage identification is very active in the current engineering field, and there are many damage identification methods. In this paper, multivariate statistical process control was introduced into the structure damage identification. First, the vibration signals from acceleration sensors were processed, time-series models(AR model) were established respectively, coefficients of AR model was used for damage identification as the basic parameter, multiway principal component analysis was used to compress and extract the AR model coefficients, and the square prediction error (SPE) values were exceeded as a sign of the existence of injury, Finally, the Euclidean distance and Mahalanobis distance of the PCA model residuals in damage identification was discussed. The experimental data obtained from a cantilever beam were used to verify the effectiveness of the method with the power of the software matlab.
Keywords: damage identification AR model multiway principal component analysis (MPCA) SPE Euclidean distance Mahalanobis distance
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