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 - 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 - CONF A1 - Viefhues, Eva A1 - Döhler, M. A1 - Simon, Patrick A1 - Herrmann, Ralf A1 - Hille, Falk A1 - Mevel, L. ED - Cunha, A. ED - Caetano, E. T1 - Stochastic subspace-based damage detection of a temperature affected beam structure T2 - Proceedings of the 10th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 10 N2 - Structural health monitoring (SHM) of civil structures often is limited due to changing environmental conditions, as those changes affect the structural dynamical properties in a similar way like damages can do. In this article, an approach for damage detection under changing temperatures is presentedand applied to a beam structure. The used stochastic subspace-based algorithm relies on a reference null space estimate, which is confronted to data from the testing state in a residual function. For damage detection the residual is evaluated by means of statistical hypothesis tests. Changes of the system due to temperature effects are handled with a model interpolation approach from linear parameter varying system theory. From vibration data measured in the undamaged state at some few reference temperatures, a model of the dynamic system valid for the current testing temperature is interpolated. The reference null space and the covariance matrix for the hypothesis test is computed from this interpolated model. This approach has been developed recently and was validated in an academic test case on simulations of a mass-spring-damper. In this paper, the approach is validated experimentally on a beam structure under varying temperature conditions in a climate chamber. Compared to other approaches, the interpolation approach leads to significantly less false positive alarms in the reference state when the structure is exposed to different temperatures, while faults can still be detected reliably. T2 - 10th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 10 CY - Online meeting DA - 30.06.2021 KW - Damage detection KW - Subspace methods KW - Temperature effects KW - Model interpolation KW - Climate chamber KW - Laboratory beam structure PY - 2021 SP - 1 EP - 6 AN - OPUS4-52999 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Viefhues, Eva A1 - Döhler, M. A1 - Hille, Falk A1 - Mevel, L. T1 - Fault detection for linear parameter varying systems under changes in the process noise covariance T2 - Preprints of the 21st IFAC World Congress (Virtual) N2 - Detecting changes in the eigenstructure of linear systems is a comprehensively investigated subject. In particular, change detection methods based on hypothesis testing using Gaussian residuals have been developed previously. In such residuals, a reference model is confronted to data from the current system. In this paper, linear output-only systems depending on a varying external physical parameter are considered. These systems are driven by process noise, whose covariance may also vary between measurements. To deal with the varying parameter, an interpolation approach is pursued, where a limited number of reference models -- each estimated from data measured in a reference state -- are interpolated to approximate an adequate reference model for the current parameter. The problem becomes more complex when the different points of interpolation correspond to different noise conditions. Then conflicts may arise between the detection of changes in the eigenstructure due to a fault and the detection of changes due to different noise conditions. For this case, a new change detection approach is developed based on the interpolation of the eigenstructure at the reference points. The resulting approach is capable of change detection when both the external physical parameter and the process noise conditions are varying. This approach is validated on a numerical simulation of a mechanical system. T2 - IFAC World Congress 2020 CY - Online meeting DA - 11.07.2020 KW - Linear parameter varying systems KW - Fault detection KW - Changing process noise KW - Subspace-based residual KW - Model interpolation PY - 2020 SP - 13858 AN - OPUS4-51617 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Viefhues, Eva A1 - Döhler, M. A1 - Hille, Falk A1 - Mevel, L. T1 - Fault detection for linear parameter varying systems under changes in the process noise covariance T2 - IFAC-PapersOnLine: 21st IFAC World Congress N2 - Detecting changes in the eigenstructure of linear systems is a comprehensively investigated subject. In particular, change detection methods based on hypothesis testing using Gaussian residuals have been developed previously. In such residuals, a reference model is confronted to data from the current system. In this paper, linear output-only systems depending on a varying external physical parameter are considered. These systems are driven by process noise, whose covariance may also vary between measurements. To deal with the varying parameter, an interpolation approach is pursued, where a limited number of reference models – each estimated from data measured in a reference state – are interpolated to approximate an adequate reference model for the current parameter. The problem becomes more complex when the different points of interpolation correspond to different noise conditions. Then conflicts may arise between the detection of changes in the eigenstructure due to a fault and the detection of changes due to different noise conditions. For this case, a new change detection approach is developed based on the interpolation of the eigenstructure at the reference points. The resulting approach is capable of change detection when both the external physical parameter and the process noise conditions are varying. This approach is validated on a numerical simulation of a mechanical system. T2 - 21st IFAC World Congress CY - Online meeting DA - 13.07.2020 KW - Linear parameter varying systems KW - Fault detectionchanging KW - Process noise KW - Subspace-based residual KW - Model interpolation PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-524652 DO - https://doi.org/10.1016/j.ifacol.2020.12.868 SN - 2405-8963 VL - 53 IS - 2 SP - 13668 EP - 13673 PB - Elsevier AN - OPUS4-52465 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bhuyan, Md Delwar Hossain A1 - Le Touz, N. A1 - Gautier, G. A1 - Döhler, M. A1 - Hille, Falk A1 - Dumoulin, J. A1 - Mevel, L. T1 - Load vector based damage localization with rejection of the temperature effect T2 - Proceedings of IOMAC'19 N2 - The Stochastic Dynamic Damage Locating Vector (SDDLV) approach is a vibration-based damage localization method based on both a finite element model of a structure and modal parameters estimated from output-only measurements in the damage and reference states. A statistical version of the Approach takes into account the inherent uncertainty due to noisy measurement data. In this paper, the effect of temperature fluctuations on the performance of the method is analyzed in a model-based approach using a finite element model with temperature dependent parameters. Robust damage localization is carried out by rejecting the temperature influence on the identified modal parameters in the damaged state. The algorithm is illustrated on a simulated structure. T2 - 8. International Operational Modal Analysis Conference CY - Kopenhagen, Denmark DA - 12. Mai 2019 KW - SDDLV KW - Load vector KW - Temperature rejection KW - Statistical evaluation PY - 2019 SP - 1 EP - 10 AN - OPUS4-48182 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bhuyan, Md Delwar Hossain A1 - Döhler, M. A1 - Lecieux, Y. A1 - Lupi, C. A1 - Thomas, J.-C. A1 - Schoefs, F. A1 - Hille, Falk A1 - Mevel, L. T1 - Statistical subspace based damage localization on Saint-Nazaire bridge mock-up T2 - Proceedings of IOMAC'19 N2 - The subject of damage localization is an important issue for Structural Health Monitoring (SHM) particularly in mechanical or civil structures under ambient excitation. In this paper, the statistical subspacebased damage localization method has been applied on a benchmark application, namely a 1/200 scale model of the Saint-Nazaire Bridge, which is a cable-stayed bridge located on the Loire River near the river’s mouth. The employed damage localization method combines data-driven features with physical parameter information from a finite element model in statistical tests, avoiding typical ill-conditioning problems of FE model updating. Damage is introduced in the mockup for cable failures on some of the 72 cables. The purpose of the experiment is to assess the capability of damage assessment methods to find a cable failure. T2 - 8. International Operational Modal Analysis Conference CY - Kopenhagen, Denmark DA - 12. Mai 2019 KW - Damage localization KW - Cable-stayed bridge KW - Cable failure KW - Structural health monitoring PY - 2019 SP - 1 EP - 9 AN - OPUS4-48183 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Viefhues, Eva A1 - Döhler, M. A1 - Zhang, Q. A1 - Hille, Falk A1 - Mevel, L. T1 - Subspace-based Damage Detection with Rejection of the Temperature Effect and Uncertainty in the Reference T2 - Proceedings of 8th International Operational Modal Analysis Conference N2 - Temperature variation can be a nuisance that perturbs vibration based structural health monitoring (SHM) approaches for civil engineering structures. In this paper, temperature affected vibration data is evaluated within a stochastic damage detection framework, which relies on a null space based residual. Besides two existing temperature rejection approaches – building a reference state from an averaging method or a piecewise method – a new approach is proposed, using model interpolation. In this approach, a general reference model is obtained from data in the reference state at several known reference temperatures. Then, for a particular tested temperature, a local reference model is derived from the general reference model. Thus, a well fitting reference null space for the formulation of a residual is available when new data is tested for damage detection at an arbitrary temperature. Particular attention is paid to the computation of the residual covariance, taking into account the uncertainty related to the null space matrix estimate. This improves the test performance, contrary to prior methods, for local and global damages, resulting in a higher probability of detection (PoD) for the new interpolation approach compared to previous approaches. T2 - International Operational Modal Analysis Conference CY - Copenhagen, Danmark DA - 13.05.2019 KW - Uncertainty KW - Statistical method KW - Subspace-based method KW - Temperature rejection KW - Model interpolation PY - 2019 SP - 1 EP - 11 AN - OPUS4-48240 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bhuyan, Md Delwar Hossain A1 - Le Touz, N. A1 - Gautier, G. A1 - Döhler, M. A1 - Hille, Falk A1 - Dumoulin, J. A1 - Mevel, L. T1 - Load Vector Based Damage Localization with Rejection of the Temperature Effect T2 - Proceedings of 8th International Operational Modal Analysis Conference N2 - The Stochastic Dynamic Damage Locating Vector (SDDLV) approach is a vibration-based damage localization method based on both a finite element model of a structure and modal parameters estimated from output-only measurements in the damage and reference states. A statistical version of the Approach takes into account the inherent uncertainty due to noisy measurement data. In this paper, the effect of temperature fluctuations on the performance of the method is analyzed in a model-based approach using a finite element model with temperature dependent parameters. Robust damage localization is carried out by rejecting the temperature influence on the identified modal parameters in the damaged state. The algorithm is illustrated on a simulated structure. T2 - International Operational Modal Analysis Conference CY - Copenhagen, Danmark DA - 13.05.2019 KW - Statistical evaluation KW - Damage localization KW - SDDLV KW - Load vector KW - Temperature rejection PY - 2019 SP - 1 EP - 10 AN - OPUS4-48241 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bhuyan, Md Delwar Hossain A1 - Döhler, M. A1 - Lecieux, Y. A1 - Lupi, C. A1 - Thomas, J. A1 - Schoefs, F. A1 - Hille, Falk A1 - Mevel, L. T1 - Statistical Subspace-based Damage Localization on Saint-Nazaire Bridge Mock-Up T2 - Proceedings of 8th International Operational Modal Analysis Conference N2 - The subject of damage localization is an important issue for Structural Health Monitoring (SHM) particularly in mechanical or civil structures under ambient excitation. In this paper, the statistical subspacebased damage localization method has been applied on a benchmark application, namely a 1/200 scale model of the Saint-Nazaire Bridge, which is a cable-stayed bridge located on the Loire River near the river’s mouth. The employed damage localization method combines data-driven features with physical parameter information from a finite element model in statistical tests, avoiding typical ill-conditioning problems of FE model updating. Damage is introduced in the mockup for cable failures on some of the 72 cables. The purpose of the experiment is to assess the capability of damage assessment methods to find a cable failure. T2 - International Operational Modal Analysis Conference CY - Copenhagen, Danmark DA - 13.05.2019 KW - Structural health monitoring KW - Damage localization KW - Cable-stayed bridge KW - Cable failure PY - 2019 SP - 1 EP - 9 AN - OPUS4-48243 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -