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Structural damage identification via a combination of blind feature extraction and sparse representation classification

机译：通过盲特征提取和稀疏表示分类相结合的结构损伤识别

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摘要

This paper addresses two problems in structural damage identification: locating damage and assessing damage severity, which are incorporated into the classification framework based on the theory of sparse representation (SR) and compressed sensing (CS). The sparsity nature implied in the classification problem itself is exploited, establishing a sparse representation framework for damage identification. Specifically, the proposed method consists of two steps: feature extraction and classification. In the feature extraction step, the modal features of both the test structure and the reference structure model are first blindly extracted by the unsupervised complexity pursuit (CP) algorithm. Then in the classification step, expressing the test modal feature as a linear combination of the bases of the over-complete reference feature dictionary-constructed by concatenating all modal features of all candidate damage classes-builds a highly underdetermined linear system of equations with an underlying sparse representation, which can be correctly recovered by ℓ_1-minimization; the non-zero entry in the recovered sparse representation directly assigns the damage class which the test structure (feature) belongs to. The two-step CP-SR damage identification method alleviates the training process required by traditional pattern recognition based methods. In addition, the reference feature dictionary can be of small size by formulating the issues of locating damage and assessing damage extent as a two-stage procedure and by taking advantage of the robustness of the SR framework. Numerical simulations and experimental study are conducted to verify the developed CP-SR method. The problems of identifying multiple damage, using limited sensors and partial features, and the performance under heavy noise and random excitation are investigated, and promising results are obtained.
机译：本文针对结构损伤识别中的两个问题：确定损伤位置和评估损伤严重程度，这两个问题都基于稀疏表示（SR）和压缩感知（CS）理论被纳入了分类框架。利用了分类问题本身所隐含的稀疏性，从而建立了用于损伤识别的稀疏表示框架。具体而言，所提出的方法包括两个步骤：特征提取和分类。在特征提取步骤中，首先通过无监督复杂度追踪（CP）算法盲目提取测试结构和参考结构模型的模态特征。然后在分类步骤中，将测试模态特征表示为超完备参考特征字典的基础的线性组合，通过将所有候选损伤类别的所有模态特征进行级联来构建，从而建立了一个高度不确定的线性方程组稀疏表示，可以通过ℓ_1最小化正确地恢复；恢复的稀疏表示中的非零条目将直接分配测试结构（功能）所属的损坏类别。两步CP-SR损伤识别方法减轻了传统基于模式识别的方法所需的训练过程。另外，参考特征字典可以通过制定损坏的位置和评估损坏程度的问题（分为两个阶段）以及利用SR框架的健壮性来实现。进行了数值模拟和实验研究，以验证所开发的CP-SR方法。研究了使用有限的传感器和局部特征来识别多重损伤，在重噪声和随机激励下的性能问题，并获得了可喜的结果。

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