Statistical subspace-based damage detection with estimated reference

The statistical subspace-based damage detection technique has shown promising theoretical and practical results for vibration-based structural health monitoring. It evaluates a residual function with efficient hypothesis testing tools, the ability of detecting small changes in chosen system parameters. In function, Hankel matrix output covariances estimated from test data is confronted to its left null space associated reference model. takes into account covariance decision making. Ideally, model assumed be perfectly known without any uncertainty, which not realistic assumption. practice, usually set avoid errors computation. Then, uncertainties may non-negligible, particular when available limited length. this paper, it investigated how distribution affected estimated. asymptotic derived, where refined term considers also uncertainty related estimate. closes gap real-world applications leads increased robustness method practice. importance including estimation numerical study on experimental progressively damaged steel frame.