Structural Damage Detection using Independent Component Analysis
Structural Damage Detection using Independent Component Analysis
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DOI：
10.1177/1475921704041876
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发表时间：
2004-03
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影响因子：
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通讯作者：
C. Zang;M. Friswell;M. Imregun
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文献类型：
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作者：
C. Zang;M. Friswell;M. Imregun
本文提出了一种基于时域数据的独立分量分析（ICA）提取和人工神经网络（ANN）相结合的结构损伤检测新方法。使用时程测量的优点是直接使用原始振动信息。然而，数据量、测量噪声和缺乏可靠的特征提取工具是主要障碍。为了规避这些问题，应用独立分量分析技术，用主要统计独立分量和混合矩阵的线性组合来表示测量数据。这种表示捕获了测量的振动数据的基本结构。混合矩阵表示的振动特征提供了测量的振动响应和独立分量之间的关系，然后用于构建用于损伤检测的简化神经网络模型。包括两个例子来证明该方法的有效性。首先，使用具有模拟位移数据的桁架结构，结果表明可以对位于九个单元的健康状态和损伤状态进行分类。其次，使用书架结构以及 24 个压电单轴加速度计测量的时间历史数据来演示物理结构上的方法。结果表明，成功检测了未损坏和损坏状态，具有非常好的准确性和可重复性。
This paper presents a novel approach to detect structural damage based on combining independent component analysis (ICA) extraction of time domain data and artificial neural networks (ANN). The advantage of using time history measurements is that the original vibration information is used directly. However, the volume of data, measurement noise and the lack of reliable feature extraction tools are the major obstacles. To circumvent them, the independent component analysis technique is applied to represent the measured data with a linear combination of dominant statistical independent components and the mixing matrix [A]. Such a representation captures the essential structure of the measured vibration data. The vibration features represented by the mixing matrix provide the relationship between the measured vibration response and the independent components and are then employed to build the simplified neural network model for damage detection. Two examples are included to demonstrate the effectiveness of the method. First, a truss structure with simulated displacement data was used, and the results show that healthy and damage states located in the nine elements may be classified. Second, a bookshelf structure together with measured time history data from 24 piezoelectric single axis accelerometers was used to demonstrate the approach on a physical structure. The results show the successful detection of the undamaged and damaged states with very good accuracy and repeatability.