Frequency response based damage detection using principal component analysis

J. Tang
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引用次数: 31

Abstract

In this paper we explore structural damage detection using frequency response signals and principal component analysis. While frequency responses are easy to measure especially in online damage detection applications, most of the associated detection methods are deterministic in nature and cannot deal with uncertainties and noise which are inevitable under practical situations. To tackle this issue and to develop a robust damage detection protocol, here we develop a feature extraction/de-noising methodology based on principal component analysis (PCA). The basic idea is to first establish a feature space of the intact structure response by using multiple measurements. Abnormal signature that is different from the baseline signature can then be identified and magnified after signal reconstruction using the intact structure features. Essentially, the directionality between an inspected signal and the baseline signal in the feature space is used as index of damage occurrence. A series of numerical analyses are performed to characterize the detection system sensitivity and robustness.
基于频率响应的主成分损伤检测
本文探讨了利用频率响应信号和主成分分析进行结构损伤检测的方法。虽然频率响应很容易测量,特别是在在线损伤检测应用中,但大多数相关的检测方法本质上是确定性的,无法处理在实际情况下不可避免的不确定性和噪声。为了解决这个问题并开发一种鲁棒的损伤检测协议,我们在这里开发了一种基于主成分分析(PCA)的特征提取/去噪方法。其基本思想是首先通过多次测量建立完整结构响应的特征空间。利用完整的结构特征对信号进行重构后,可以识别和放大与基线特征不同的异常特征。本质上,被检测信号与特征空间中基线信号之间的方向性被用作损伤发生的指标。通过一系列的数值分析来表征检测系统的灵敏度和鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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