Transmissibility function (TF) is widely applied in damage detection due to its sensitivity to damage and robustness to external excitations, but its application in online damage detection is rarely reported due to challenges in handling data streams. This study proposes a new TF-based online damage detection method that integrates a truncation-free variational inference-based full Dirichlet process Gaussian mixture model (VI-FDPGMM) within a streaming variational inference (SVI) paradigm. As an improved Bayesian nonparametric approach, the truncation-free VI-FDPGMM addresses the issue of truncating mixing components in traditional VI-DPGMM for online learning with increasing data by strategically setting the variational distributions of parameters for the components without assigned data (i.e., inactivated components) to their prior distributions based on the Bayesian viewpoint, which enables computing the probabilities to assign data points to these components and determining the creation of new components. As a result, the truncation-free VI-FDPGMM allows dynamically adding components to the mixture model, providing the flexibility to automatically adapt the number of components for arbitrary amounts of data. This characteristic enables its intuitive integration into the SVI paradigm featured as the variational posterior conditioned on the previous data as the prior when new data are observed, facilitating continuous refinement of the mixture model without repeatedly making inference of previous data. Therefore, the proposed method is highly efficient and well-suited for online damage detection. The proposed method is validated using two case studies, demonstrating its capability to dynamically generate new clusters as new data are available online to indicate the emergence of new damage patterns during the monitoring process, which enables it to perform structural anomaly detection tasks in a semi-supervised manner. Furthermore, the method outperforms some state-of-the-art methods due to its capability for continuous model refinement and robustness in interpreting and capturing uncertainties.

中文翻译：

---

流式变分推理授权贝叶斯非参数聚类，用于具有传递函数的在线结构损伤检测


传递函数（TF）由于其对损伤的敏感性和对外部激励的鲁棒性而被广泛应用于损伤检测，但由于处理数据流的挑战，其在在线损伤检测中的应用很少被报道。本研究提出了一种新的基于 TF 的在线损伤检测方法，该方法将基于无截断变分推理的全狄利克雷过程高斯混合模型 (VI-FDPGMM) 集成到流变分推理 (SVI) 范式中。作为一种改进的贝叶斯非参数方法，无截断 VI-FDPGMM 解决了传统 VI-DPGMM 中截断混合组件的问题，以便通过策略性地设置没有分配数据的组件的参数变分分布（即，基于贝叶斯观点的先验分布，这使得能够计算将数据点分配给这些组件的概率并确定新组件的创建。因此，无截断的 VI-FDPGMM 允许动态地将组件添加到混合模型中，从而提供了自动调整组件数量以适应任意数据量的灵活性。这一特性使其能够直观地集成到 SVI 范式中，该范式的特点是当观察到新数据时，以先前数据为先验条件的变分后验，有利于混合模型的持续细化，而无需重复对先前数据进行推断。因此，该方法效率很高，非常适合在线损伤检测。 使用两个案例研究对所提出的方法进行了验证，证明了其随着新数据在线可用而动态生成新集群的能力，以指示监测过程中新损坏模式的出现，这使其能够以半半自动方式执行结构异常检测任务。监督方式。此外，该方法由于其连续模型细化的能力以及解释和捕获不确定性的鲁棒性而优于一些最先进的方法。