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首页> 外文期刊>Mechanical systems and signal processing >Robust damage localization in plate-type structures by using an enhanced robust principal component analysis and data fusion technique
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Robust damage localization in plate-type structures by using an enhanced robust principal component analysis and data fusion technique

机译：通过使用增强的鲁棒主成分分析和数据融合技术，平板结构中的强大损坏定位

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摘要

Damage localization in plate-type structures via full-field vibration measurements has attracted much more attention. Traditionally, the damage-induced local shape singularities at a certain mode are harnessed for damage localization, but this is not reliable and robust for multi-damage localization. Therefore, a general strategy is that the damage features in different modes should be accurately extracted and integrated for a robust damage localization. However, the damage features are naturally contaminated by the measurement noise and the baseline-data on pristine state is commonly unavailable, which degrade the accuracy of damage feature extraction. Furthermore, the damage features in different modes normally contain conflicting damage location evidence, which leads to misleading damage localization results. To address these issues, an enhanced robust principal component analysis (RPCA) with contiguous outlier constraint is proposed to accurately extract the damage-caused local features without requiring the baseline-data of healthy state. Moreover, a novel data fusion approach based on cosine similarity measure is developed to effectively integrate the damage features of different modes for robust damage localization. In addition, a multiscale denoising approach is proposed to evaluate the noise-robust full-field vibration measurements for damage localization. Finally, numerical and experimental studies of cantilever plates with two damage zones are studied to verify the feasibility and effectiveness of the proposed damage localization method. It is found that the proposed damage localization method is robust in two aspects: damage feature extraction from noisy measurements and detecting all the possible damage zones.
机译：通过全场振动测量的板式结构造成损坏的定位已经吸引了更多的关注。传统上，在某种模式下造成局部形状奇异性被利用损坏定位，但对于多灾定位来说是不可靠和稳健的。因此，一般策略是，应准确地提取和集成不同模式的损坏特征，以实现强大的损坏本地化。然而，损坏特征自然被测量噪声污染，并且原始状态上的基线数据通常是不可用的，这降低了损坏特征提取的准确性。此外，不同模式的损坏特征通常包含冲突的损伤位置证据，这导致误导损坏定位结果。为了解决这些问题，提出了一种具有连续异常约束的增强的强大主成分分析（RPCA），以准确提取损坏导致的本地特征，而不需要健康状态的基线数据。此外，开发了一种基于余弦相似度量的新型数据融合方法，以有效地整合不同模式的损坏特征，以实现鲁棒损坏定位。此外，提出了一种多尺度去噪方法来评估损坏定位的噪声稳健的全场振动测量。最后，研究了具有两个损伤区域的悬臂板的数值和实验研究，验证了所提出的损伤定位方法的可行性和有效性。结果发现，所提出的损坏定位方法在两个方面是强大的：损坏特征提取从嘈杂的测量和检测所有可能的损坏区域。

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