The influence of varying environmental/operational situations on damage‐sensitive properties is a vital problem when the vibration responses are employed for structural damage detection. In this work, singular spectrum analysis (SSA) and the statistical control chart are combined to develop a new damage identification approach for structural damage detection under variable environmental/operational situations. The SSA is utilized to decompose the measured frequency sequence into the sum of independent components, including a tardily changing trend, oscillatory, and noise components. The trend‐related decomposed components can be identified by finding the corresponding slow‐varying eigenvectors. The trend components induced by varying environmental/operational conditions are discarded, while the remaining ones, including oscillatory and noise components, are selected to reconstruct the new frequency sequence. Hence, the impact of variable environmental/operational situations on the original frequency sequence is removed. The reconstructed frequency shift from its reference status (undamaged status) is utilized to construct the damage features, which are employed as the control chart samples. Accordingly, the damage features obtained from the undamaged status are utilized to calculate the control limits. The subsequent damage indices in unknown status are monitored considering the control limits. A considerable number of damage indices out of the control limits range show a system transformation from an undamaged status to a damaged one. The feasibility of the presented approach is analytically and experimentally evaluated through an offshore platform in the presence of a white noise excitation.

中文翻译：

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基于奇异谱分析和统计控制图的可变环境/运行条件下的结构损伤识别

当将振动响应用于结构损伤检测时，变化的环境/操作情况对损伤敏感特性的影响是一个至关重要的问题。在这项工作中，将奇异频谱分析（SSA）和统计控制图结合起来，以开发一种新的损伤识别方法，用于在变化的环境/运行情况下进行结构损伤检测。SSA用于将测得的频率序列分解为独立分量的总和，包括延迟变化的趋势，振荡分量和噪声分量。可以通过找到相应的慢变化特征向量来识别与趋势相关的分解分量。丢弃由变化的环境/操作条件引起的趋势分量，而其余的趋势分量，选择包括振荡和噪声成分的信号来重构新的频率序列。因此，消除了可变的环境/运行状况对原始频率序列的影响。从参考状态（未损坏状态）重构的频移用于构建损坏特征，将其用作控制图样本。因此，利用从未损坏状态获得的损坏特征来计算控制极限。考虑到控制极限，对处于未知状态的后续损坏指数进行监视。超出控制极限范围的相当数量的损坏指数表明系统已从未损坏状态转变为损坏状态。