Enormous data are continuously collected by the structural health monitoring system of civil infrastructures. The structural health monitoring data inevitably involve anomalies caused by sensors, transmission errors, or abnormal structural behaviors. It is important to identify the anomalies and find their origin (e.g. sensor fault or structural damage) to make correct interventions. Moreover, online anomaly identification of the structural health monitoring data is critical for timely structural condition assessment and decision-making. This study proposes an online approach for detecting anomalies of the structural health monitoring data based on the Bayesian dynamic linear model. In particular, Bayesian dynamic linear model, consisting of various components, is implemented to characterize the feature of real-time measurements. Expectation maximization algorithm and Kalman smoother are combined to estimate the Bayesian dynamic linear model parameters and generate log-likelihood functions. The subspace identification method is introduced to overcome the initialization issue of the expectation maximization algorithm. The log-likelihood difference of consecutive time steps is then used to determine thresholds without introducing extra anomaly detectors. The proposed Bayesian dynamic linear model-based approach is first illustrated by the simulation data and then applied to the structural health monitoring data collected from two long-span bridges. The results indicate that the proposed method exhibits good accuracy and high computational efficiency and also allows for reconstructing the strain measurements to replace anomalies.

民间基础设施的结构健康监测系统不断收集大量数据。结构健康监测数据不可避免地涉及由传感器，传输错误或异常结构行为引起的异常。识别异常并找到其来源（例如传感器故障或结构损坏）以进行正确的干预非常重要。此外，在线异常识别结构健康监测数据对于及时进行结构状况评估和决策至关重要。本研究提出了一种基于贝叶斯动态线性模型的在线检测结构健康监测数据异常的方法。特别是，实现了由各种组件组成的贝叶斯动态线性模型来表征实时测量的特征。期望最大化算法和卡尔曼平滑器相结合，以估计贝叶斯动态线性模型参数并生成对数似然函数。引入子空间识别方法来克服期望最大化算法的初始化问题。然后，将连续时间步长的对数似然差用于确定阈值，而无需引入额外的异常检测器。所提出的基于贝叶斯动态线性模型的方法首先由仿真数据说明，然后应用于从两座大跨度桥梁收集的结构健康监测数据。结果表明，所提出的方法具有良好的准确性和较高的计算效率，并且还允许重建应变测量值以替代异常。