Structures are subjected to various kinds of structural deterioration and damage with use over long periods of service life. For the safety assurance of structures, it is very important to have a long‐term monitoring system and continuous assessments of structural integrity using the measured data. The objective of this paper is to develop an anomaly detection algorithm for a long‐term structural health monitoring (SHM) system based on probabilistic principal component analysis (PPCA). Static stress data were measured and used in this monitoring system. A baseline PPCA model is built under various environmental loading conditions. Then, newly monitored data are projected onto the principal vectors. Anomaly indices and their probability distributions are evaluated to determine the presence of structural damage indicated by outliers. This method is also capable of dealing with incomplete data and recovering the missing data. First, numerical simulation studies of a revolving auditorium are carried out to validate the proposed PPCA‐based method. Then, real monitoring data collected from the SHM system are used to detect the presence and locations of anomalies in the revolving auditorium.

中文翻译：

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基于概率主成分分析的数据缺失结构异常检测

在长期使用寿命中使用的结构会遭受各种类型的结构破坏和损坏。为了保证结构的安全，拥有长期的监控系统并使用测量数据对结构完整性进行连续评估非常重要。本文的目的是开发基于概率主成分分析（PPCA）的长期结构健康监测（SHM）系统的异常检测算法。测量了静态应力数据，并在此监控系统中使用了该数据。在各种环境负荷条件下建立基线PPCA模型。然后，将新监视的数据投影到主向量上。对异常指数及其概率分布进行评估，以确定异常值指示的结构损坏的存在。此方法还能够处理不完整的数据并恢复丢失的数据。首先，进行了旋转礼堂的数值模拟研究，以验证所提出的基于PPCA的方法。然后，从SHM系统收集的真实监控数据将用于检测旋转礼堂中异常的存在和位置。