Tracking the temporal variation of the properties of a system is relevant in different settings when data of extended duration is available, e.g., anomaly detection, condition monitoring, and trend identification. One simple approach is to divide the data into non-overlapping segments and then identify the model properties of each segment individually using a time-invariant model within the segment. The potential change of the system is then investigated by tracking the variations of model properties from one segment to another. In this context, a dynamic programming approach has been developed recently that determines the ‘best partitioning’ of data segments, between which a change of model parameters takes place in the sense of Bayesian model selection. As a change can result when any one of the parameters has changed in a statistically significant manner, a basic question is concerned with what constitutes a suitable model that meets specific monitoring objectives. E.g., should all or only some of the parameters be allowed to change? Motivated by this, a quasi time-invariant (QTI) modeling methodology is proposed in this work where only some (rather than all) parameters are allowed to change across data segments. Computational issues associated with this new class of models are addressed, e.g., the efficient calculation of posterior most probable value and covariance matrix, and Bayesian evidence in the context of dynamic programming. Focusing on modal property (natural frequencies, damping ratios, etc.) tracking with ambient data, the proposed methodology with QTI model is investigated with synthetic and laboratory data; and applied to field data of a tall building during a typhoon event. The results from field data are compared with those from existing methods.

中文翻译：

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使用贝叶斯动态规划的准时不变模型跟踪时变属性


当长时间的数据可用时，跟踪系统属性的时间变化在不同的设置中是相关的，例如异常检测、状态监控和趋势识别。一种简单的方法是将数据划分为不重叠的段，然后使用段内的时不变模型单独识别每个段的模型属性。然后通过跟踪模型属性从一个部分到另一部分的变化来研究系统的潜在变化。在这种背景下，最近开发了一种动态规划方法，它确定数据段的“最佳划分”，在贝叶斯模型选择的意义上，模型参数的变化发生在数据段之间。由于当任何一个参数以统计上显着的方式发生变化时都可能导致变化，因此一个基本问题涉及什么构成满足特定监测目标的合适模型。例如，应该允许更改全部参数还是仅允许更改部分参数？受此启发，本工作提出了一种准时不变（QTI）建模方法，其中仅允许一些（而不是全部）参数跨数据段进行更改。解决了与此类新模型相关的计算问题，例如后验概率值和协方差矩阵的有效计算，以及动态规划背景下的贝叶斯证据。重点关注环境数据的模态特性（固有频率、阻尼比等）跟踪，利用合成数据和实验室数据对所提出的 QTI 模型方法进行了研究；并应用于台风期间高层建筑的现场数据。将现场数据的结果与现有方法的结果进行比较。