The early detection of anomalies associated with changes in the behavior of structures is important for ensuring their serviceability and safety. Identifying anomalies from monitoring data is prone to false and missed alarms due to the uncertain nature of the infrastructure responses' dependency on external factors such as temperature and loading. Existing anomaly detection strategies typically rely on univariate threshold values and disregard the planning horizon in the context of decision making. This paper proposes an anomaly detection framework that combines the interpretability of existing Bayesian dynamic linear models, a particular form of state‐space models, with the long‐term planning ability of reinforcement learning. The new framework provides (a) reinforcement learning formalism for anomaly detection in Bayesian dynamic linear models, (b) a method for simulating anomalies with respect to its height, duration, and time of occurrence, and (c) a method for quantifying anomaly detectability. The potential of the new framework is demonstrated on monitoring data collected on a bridge in Canada. The results show that the framework is able to detect real anomalies that were known to have occurred, as well as synthetic anomalies.

中文翻译：

---

使用状态空间模型和强化学习进行异常检测

尽早发现与结构行为变化相关的异常对于确保其可维护性和安全性很重要。由于基础架构响应依赖于外部因素（例如温度和负载）的不确定性，因此从监视数据中识别异常很容易导致误报和漏报。现有的异常检测策略通常依赖于单变量阈值，并且在决策过程中无视规划范围。本文提出了一种异常检测框架，该框架将现有贝叶斯动态线性模型（一种特殊形式的状态空间模型）的可解释性与强化学习的长期计划能力相结合。新框架提供了（a）贝叶斯动态线性模型中异常检测的强化学习形式，（b）一种针对异常的高度，持续时间和发生时间进行模拟的方法，以及（c）量化异常可检测性的方法。通过监测加拿大一座桥梁上收集的数据，证明了新框架的潜力。结果表明，该框架能够检测已知已发生的实际异常以及合成异常。