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Localization and quantification of damage in beam-like structures using sensitivities of principal component analysis results

机译：利用主成分分析结果的灵敏度对梁状结构中的损伤进行定位和量化

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摘要

Principal component analysis (PCA) is known as an efficient method for dynamic system identification and diagnosis. This paper addresses a damage diagnosis method based on sensitivities of PCA in the frequency domain for linear-form structures. The aim is not only to detect the presence of damage, but also to localize and to evaluate it. The Frequency response functions measured at different locations on the beam are considered as data for the PCA process. Sensitivities of principal components obtained from PCA to beam parameters are computed and inspected according to the location of sensors; their variation from the healthy state to the damaged state indicates damage locations. The damage can be evaluated next providing that a structural model is available; this evaluation is based on a model updating procedure. It is worth noting that the diagnosis process does not require a modal identification achievement. Both numerical and experimental examples are used for better illustration.
机译：主成分分析（PCA）是动态系统识别和诊断的有效方法。本文针对线性形式结构，基于频域中PCA的灵敏度提出了一种损伤诊断方法。目的不仅是检测损坏的存在，而且要进行定位和评估。在光束的不同位置测得的频率响应函数被视为PCA过程的数据。根据传感器的位置，计算并检查从PCA获得的主成分对光束参数的敏感性；它们从健康状态到损坏状态的变化指示损坏位置。如果可以使用结构模型，则可以在接下来评估损坏。此评估基于模型更新过程。值得注意的是，诊断过程不需要模态识别成就。数值示例和实验示例均用于更好地说明。

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