TY - CONF A1 - Viefhues, Eva A1 - Döhler, M. A1 - Hille, Falk A1 - Mevel, L. T1 - Stochastic subspace-based damage detection with uncertainty in the reference null space T2 - Proceedings of the 11th International Workshop on Structural Health Monitoring 2017: Real-Time State Awareness and Data-Driven Safety Assurance N2 - This paper deals with uncertainty considerations in damage diagnosis using the stochastic subspace-based damage detection technique. With this method, a model is estimated from data in a (healthy) reference state and confronted to measurement data from the possibly damaged state in a hypothesis test. Previously, only the uncertainty related to the measurement data was considered in this test, whereas the uncertainty in the estimation of the reference model has not been considered. We derive a new test framework, which takes into account both the uncertainties in the estimation of the reference model as well as the uncertainties related to the measurement data. Perturbation theory is applied to obtain the relevant covariances. In a numerical study the effect of the new computation is shown, when the reference model is estimated with different accuracies, and the performance of the hypothesis tests is evaluated for small damages. Using the derived covariance scheme increases the probability of detection when the reference model estimate is subject to high uncertainty, leading to a more reliable test. T2 - International Workshop on Structural Health Monitoring CY - Stanford, CA, USA DA - 12.09.2017 KW - Subspace-methods KW - Uncertainty KW - Damage detection PY - 2017 SN - 978-1-60595-330-4 SP - 1007 EP - 1014 AN - OPUS4-42731 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -