TY - JOUR A1 - Mendler, A. A1 - Döhler, M. A1 - Hille, Falk T1 - Detecting changes in boundary conditions based on sensitivity-based statistical tests JF - E-journal of nondestructive testing N2 - Structural health monitoring is a promising technology to automatically detect structural changes based on permanently installed sensors. Vibration-based methods that evaluate the global system response to ambient excitation are suited to diagnose changes in boundary conditions, i.e., changes in member prestress or imposed displacements. In this paper, these changes are evaluated based on sensitivity-based statistical tests, which are capable of detecting and localizing parametric structural changes. The main contribution is the analytical calculation of sensitivity vectors for changes in boundary conditions (i.e., changes in prestress or support conditions) based on stress stiffening, and the combination with a numerically efficient algorithm, i.e., Nelson’s method. One of the main advantages of the employed damage diagnosis algorithm is that, although it uses physical models for damage detection, it considers the uncertainty in the data-driven features, which enables a reliabilitybased approach to determine the probability of detection. Moreover, the algorithm can be trained and the probability of detecting future damages can be predicted based on data and a model from the undamaged structure, in an unsupervised learning mode, making it particularly relevant for unique structures, where no data from the damaged state is available. For proof of concept, a numerical case study is presented. The study assesses the loss of prestress in a two-span reinforced concrete beam and showcases suitable validation approaches for the sensitivity calculation. T2 - International Symposium on Non-Destructive Testing in Civil Engineering (NDT-CE 2022) CY - Zurich, Switzerland DA - 16.08.2022 KW - Nelson’s method KW - Global ambient vibrations KW - Asymptotic local approach KW - Sensitivity vectors KW - Probability of detection KW - Stress stiffening PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-566632 DO - https://doi.org/10.58286/27311 SN - 1435-4934 VL - 27 IS - 9 SP - 1 EP - 13 PB - NDT.net CY - Kirchwald AN - OPUS4-56663 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Viefhues, Eva A1 - Döhler, M. A1 - Hille, Falk A1 - Mevel, L. T1 - Statistical subspace-based damage detection with estimated reference JF - Mechanical Systems and Signal Processing N2 - The statistical subspace-based damage detection technique has shown promising theoretical and practical results for vibration-based structural health monitoring. It evaluates a subspacebased residual function with efficient hypothesis testing tools, and has the ability of detecting small changes in chosen system parameters. In the residual function, a Hankel matrix of Output covariances estimated from test data is confronted to its left null space associated to a reference model. The hypothesis test takes into account the covariance of the residual for decision making. Ideally, the reference model is assumed to be perfectly known without any uncertainty, which is not a realistic assumption. In practice, the left null space is usually estimated from a reference data set to avoid model errors in the residual computation. Then, the associated uncertainties may be non-negligible, in particular when the available reference data is of limited length. In this paper, it is investigated how the statistical distribution of the residual is affected when the reference null space is estimated. The asymptotic residual distribution is derived, where its refined covariance term considers also the uncertainty related to the reference null space estimate. The associated damage detection test closes a theoretical gap for real-world applications and leads to increased robustness of the method in practice. The importance of including the estimation uncertainty of the reference null space is shown in a numerical study and on experimental data of a progressively damaged steel frame. KW - Damage detection KW - Uncertainty quantification KW - Statistical tests KW - Ambient excitation KW - Vibration measurement PY - 2022 DO - https://doi.org/10.1016/j.ymssp.2021.108241 SN - 0888-3270 VL - 164 SP - 108241 PB - Elsevier Ltd. AN - OPUS4-52998 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Bhuyan, Md Delwar Hossain A1 - Gautier, G. A1 - Le Touz, N. A1 - Döhler, M. A1 - Hille, Falk A1 - Dumoulin, J. A1 - Mevel, L. T1 - Vibration‐based damage localization with load vectors under temperature changes JF - Structural Control and Health Monitoring N2 - Damage detection and localization in civil or mechanical structures is a subject of active development and research. A few vibration‐based methods have been developed so far, requiring, for example, modal parameter estimates in the reference and damaged states of the investigated structure, and for localization in addition a finite element model. For structures in operation, temperature has been shown to be a major nuisance to the efficiency of such methods because the modal parameters are varying not only with damage but also due to temperature variations. For detection, a few rejection approaches have been developed. Besides the increased complexity, environmental variation is hardly taken into account in localization approaches. In this paper, we propose a sensitivity‐based correction of the identified modal parameters in the damaged state with respect to the temperature field in the reference state, on the basis of a sensitivity analysis with respect to temperature dependent Parameters of the finite element model in the reference state. The approach is then applied to the stochastic dynamic damage locating vector method, where its improved performance under nonuniform temperature variations is shown in a numerical application on a beam. KW - Statistical evaluation KW - Damage localization KW - Load vector KW - Temperature effect rejection KW - Temperature modeling PY - 2019 DO - https://doi.org/10.1002/stc.2439 SN - 1545-2255 SN - 1545-2263 SP - e2439, 1 EP - 16 PB - John Wiley & Sons, Ltd. AN - OPUS4-48843 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -