A Robust Probabilistic Approach to Stochastic Subspace Identification

TL;DR

This paper introduces a probabilistic formulation of stochastic subspace identification (SSI) that enhances robustness against outliers in field measurements, improving modal parameter estimation accuracy in operational modal analysis.

Contribution

It presents a novel probabilistic approach to SSI, enabling the development of a robust algorithm that automatically handles anomalous data in operational modal analysis.

Findings

Robust Prob-SSI outperforms conventional SSI with corrupted data.

Enhanced confidence in pole estimation demonstrated on benchmark data.

Probabilistic formulation provides a mathematical foundation for advanced OMA methods.

Abstract

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.