Abstract While the concept of structural monitoring has been around for a number of decades, it remains under-exploited in practice. A main driver for this shortcoming lies in the difficulty to robustly and autonomously interpret the information that is extracted from dynamic data. This hindrance in properly deciphering the collected information may be attributed to the uncertainty that is inherent in i) the finite set of measured data, ii) the models employed for capturing the manifested dynamics, and more importantly, iii) the susceptibility of these systems to variations in Environmental and Operational Parameters (EOPs). In previous work of the authors, a Gaussian Process (GP) time-series approach has been introduced, which serves as a hierarchical input–output method to account for the influence of EOPs on structural response. This in turn enables a robust structural identification. In this scheme, the short-term dynamics are modeled by means of linear-in-the-parameters time-series models, while EOV dependence – acting on a long-term time scale – is achieved via GP regression of the model coefficients on measured EOPs. This work corresponds to a further advancement on this modeling approach, corresponding to its generalization to the vector response case. Particularly, the problem of global identification here is solved via an Expectation–Maximization algorithm tailored to the GP time-series model structure. Moreover, an EOP-dependent innovations covariance matrix is integrated in the model, which helps to capture variation in the vibration power. The resulting model does not only have the capability to represent the long-term response of a structure under variable EOPs, but also facilitates the enhanced tracking of modal quantities in contrast to traditional operational modal analysis techniques. The proposed approach is exemplified on the identification of the vibration response of a simulated wind turbine blade at different points along the blade axis in the flap-wise direction, under variability of both the acting wind speeds and ambient temperatures.

中文翻译：

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多变量 GP-VAR 模型，用于在操作可变性下进行稳健的结构识别

摘要 虽然结构监测的概念已经存在了几十年，但在实践中仍未得到充分利用。这一缺点的主要驱动因素在于难以稳健和自主地解释从动态数据中提取的信息。正确破译收集到的信息的这种障碍可能归因于 i) 有限的测量数据集，ii) 用于捕获表现的动态的模型，以及更重要的是，iii) 这些系统对这些系统的敏感性环境和操作参数 (EOP) 的变化。在作者之前的工作中，已经引入了高斯过程 (GP) 时间序列方法，该方法作为分层输入输出方法来解释 EOP 对结构响应的影响。这反过来又实现了稳健的结构识别。在这个方案中，短期动态是通过线性参数时间序列模型建模的，而 EOV 依赖——作用于长期时间尺度——是通过模型系数的 GP 回归实现的EOP。这项工作对应于这种建模方法的进一步进步，对应于其对向量响应情况的推广。特别是，这里的全局识别问题是通过针对 GP 时间序列模型结构量身定制的期望最大化算法来解决的。此外，模型中集成了依赖于 EOP 的创新协方差矩阵，这有助于捕捉振动功率的变化。由此产生的模型不仅能够表示结构在可变 EOP 下的长期响应，而且与传统的操作模态分析技术相比，还有助于增强模态量的跟踪。在作用风速和环境温度的可变性下，所提出的方法在沿叶片轴线在襟翼方向的不同点处的模拟风力涡轮机叶片的振动响应的识别中得到了例证。