Modal parameter estimation of operational structures is often a challenging task when confronted with unwanted distortions (outliers) in field measurements. Atypical observations present a problem to operational modal analysis (OMA) algorithms, such as stochastic subspace identification (SSI), severely biasing parameter estimates and resulting in misidentification of the system. Despite this predicament, no simple mechanism currently exists capable of dealing with such anomalies in SSI. Addressing this problem, this paper first introduces a novel probabilistic formulation of stochastic subspace identification (Prob-SSI), realised using probabilistic projections. Mathematically, the equivalence between this model and the classic algorithm is demonstrated. This fresh perspective, viewing SSI as a problem in probabilistic inference, lays the necessary mathematical foundation to enable a plethora of new, more sophisticated OMA approaches. To this end, a statistically robust SSI algorithm (robust Prob-SSI) is developed, capable of providing a principled and automatic way of handling outlying or anomalous data in the measured timeseries, such as may occur in field recordings, e.g. intermittent sensor dropout. Robust Prob-SSI is shown to outperform conventional SSI when confronted with 'corrupted' data, exhibiting improved identification performance and higher levels of confidence in the found poles when viewing consistency (stabilisation) diagrams. Similar benefits are also demonstrated on the Z24 Bridge benchmark dataset, highlighting enhanced performance on measured systems.

翻译：运行结构模态参数估计在现场测量中遇到非预期失真（异常值）时通常是一项艰巨任务。非典型观测值对运行模态分析（OMA）算法（如随机子空间辨识SSI）构成问题，会使参数估计产生严重偏差并导致系统误识别。尽管存在这一困境，目前尚无简单机制能够处理SSI中的此类异常。针对该问题，本文首先引入了一种基于概率投影实现的随机子空间辨识新概率公式（Prob-SSI）。从数学上证明了该模型与经典算法之间的等价性。这种将SSI视为概率推断问题的新视角，为开发更多更先进的OMA方法奠定了必要的数学基础。为此，我们开发了一种统计鲁棒的SSI算法（鲁棒Prob-SSI），能够以原理性且自动化的方式处理测量时间序列中的离群或异常数据（如现场记录中可能出现间歇性传感器失效的情况）。当面对"污染"数据时，鲁棒Prob-SSI展现出优于传统SSI的性能，在一致性（稳定）图中表现出更高的辨识精度和更强的极点置信度。Z24桥梁基准数据集上的测试结果同样验证了该方法的优越性能，突显了在实测系统上的增强表现。