Abstract The development of a fully automated system identifier without the need for human intervention, is a key step for real-time vibration-based Structural Health Monitoring (SHM). In this paper a novel approach based on the Dirichlet Process Gaussian Mixture Model (DP-GMM) is developed in order to analyze the stabilization diagram. The aim is to separate the true physical modes from the mathematically spurious modes in a fully automated manner, whilst eliminating the need for any manually specified parameters or thresholds. The parametric Covariance Driven Stochastic Subspace Identification (SSI-Cov) is adopted to estimate the modal parameters, and consequently establish the initial stabilization diagram. From there, the use of a two-stage algorithm involving a DP-GMM is proposed to non-parametrically perform an automated cleaning of the stabilization diagram. The contributions of the paper are five-fold: (1) A probabilistic approach based on a DP-GMM is proposed to analyze the stabilization diagram. To the best knowledge of the authors, this study presents one of the first attempts of DP-GMM for full automation of Operational Modal Analysis (OMA). The method is validated using the field test data from a large-scale operating cable-stayed bridge, which has two closely-spaced modes around 3 Hz. Not only are these two complicated scenarios consistently identified, but the performance of the method with respect to the problem of missing modes is compared against a reference method based on the conventional multi-stage clustering technique used in OMA, wherein superior performance of the proposed method is demonstrated. (2) The method does not require specification of any threshold or parameter at any stage of the algorithm for cleaning the stabilization diagram, making the approach a potential for robust and fully automated modal identification. (3) Compared to many conventional multi-stage clustering techniques, the proposed approach is computationally efficient as intelligent updates are made to the model using multiple linear algebra properties. (4) New feature vectors are developed which are justified using a combination of mathematical rigor, visual understanding, and engineering intuition. (5) Due to the probabilistic nature of the method, the identification results are accompanied with uncertainty bounds. Several mathematical proofs are presented to explain the observed behavior of the uncertainty bounds.

中文翻译：

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用于操作模态分析的无限混合模型：一种自动化和有原则的方法

摘要 无需人工干预的全自动系统标识符的开发是基于振动的实时结构健康监测 (SHM) 的关键步骤。在本文中，开发了一种基于狄利克雷过程高斯混合模型 (DP-GMM) 的新方法来分析稳定图。目的是以全自动方式将真实物理模式与数学上的虚假模式分开，同时消除对任何手动指定参数或阈值的需要。采用参数协方差驱动随机子空间识别（SSI-Cov）估计模态参数，从而建立初始稳定图。从那里，建议使用涉及 DP-GMM 的两阶段算法以非参数方式执行稳定图的自动清理。该论文的贡献有五方面：（1）提出了一种基于 DP-GMM 的概率方法来分析稳定图。据作者所知，本研究展示了 DP-GMM 用于完全自动化操作模态分析 (OMA) 的首次尝试之一。该方法使用来自大型运营斜拉桥的现场测试数据进行验证，斜拉桥具有大约 3 Hz 的两种紧密间隔模式。这两个复杂的场景不仅一致地被识别，而且该方法在丢失模式问题方面的性能与基于 OMA 中使用的传统多级聚类技术的参考方法进行了比较，其中证明了所提出方法的优越性能。(2) 该方法不需要在用于清理稳定图的算法的任何阶段指定任何阈值或参数，使该方法成为鲁棒和全自动模态识别的潜力。(3) 与许多传统的多阶段聚类技术相比，所提出的方法在计算上是高效的，因为使用多个线性代数属性对模型进行了智能更新。(4) 开发了新的特征向量，这些特征向量使用数学严谨性、视觉理解和工程直觉的组合来证明是合理的。(5) 由于该方法的概率性质，识别结果伴随着不确定性界限。