Vibration-based damage localisation: Impulse response identification and model updating methods

Structural health monitoring has gained more and more interest over the recent decades. As the technology has matured and monitoring systems are employed commercially, the development of more powerful and precise methods is the logical next step in this field. Especially vibration sensor networks with few measurement points combined with utilisation of ambient vibration sources are attractive for practical applications, as this approach promises to be cost-effective while requiring minimal modification to the monitored structures. Since efficient methods for damage detection have already been developed for such sensor networks, the research focus shifts towards extracting more information from the measurement data, in particular to the localisation and quantification of damage. Two main concepts have produced promising results for damage localisation. The first approach involves a mechanical model of the structure, which is used in a model updating scheme to find the damaged areas of the structure. Second, there is a purely data-driven approach, which relies on residuals of vibration estimations to find regions where damage is probable. While much research has been conducted following these two concepts, different approaches are rarely directly compared using the same data sets. Therefore, this thesis presents advanced methods for vibration-based damage localisation using model updating as well as a data-driven method and provides a direct comparison using the same vibration measurement data. The model updating approach presented in this thesis relies on multiobjective optimisation. Hence, the applied numerical optimisation algorithms are presented first. On this basis, the model updating parameterisation and objective function formulation is developed. The data-driven approach employs residuals from vibration estimations obtained using multiple-input finite impulse response filters. Both approaches are then verified using a simulated cantilever beam considering multiple damage scenarios. Finally, experimentally obtained data from an outdoor girder mast structure is used to validate the approaches. In summary, this thesis provides an assessment of model updating and residual-based damage localisation by means of verification and validation cases. It is found that the residual-based method exhibits numerical performance sufficient for real-time applications while providing a high sensitivity towards damage. However, the localisation accuracy is found to be superior using the model updating method

Very low frequency IEPE accelerometer calibration and application to a wind energy structure

In this work, we present an experimental setup for very low frequency calibration measurements of low-noise integrated electronics piezoelectric (IEPE) accelerometers and a customised signal conditioner design for using IEPE sensors down to 0.05 Hz. AC-response IEPE accelerometers and signal conditioners have amplitude and phase deviations at low frequencies. As the standard calibration procedure in the low-frequency range is technically challenging, IEPE accelerometers with standard signal conditioners are usually used in frequency ranges above 1 Hz. Vibrations on structures with low eigenfrequencies like wind turbines are thus often monitored using DC-coupled micro-electro-mechanical system (MEMS) capacitive accelerometers. This sensor type suffers from higher noise levels compared to IEPE sensors. To apply IEPE sensors instead of MEMS sensors, in this work the calibration of the entire measurement chain of three different IEPE sensors with the customised signal conditioner is performed with a low-frequency centrifuge. The IEPE sensors are modelled using infinite impulse response (IIR) filters to apply the calibration to time-domain measurement data of a wind turbine support structure. This procedure enables an amplitude and phase-accurate vibration analysis with IEPE sensors in the low-frequency range down to 0.05 Hz

Multi-Objective Global Pattern Search: Effective numerical optimisation in structural dynamics

With this work, a novel derivative-free multi-objective optimisation approach for solving engineering problems is presented. State-of-the-art algorithms usually require numerical experimentation in order to tune the algorithm’s multiple parameters to a specific optimisation problem. This issue is effectively tackled by the presented deterministic method which has only a single parameter. The most popular multi-objective optimisation algorithms are based on pseudo-random numbers and need several parameters to adjust the associated probability distributions. Deterministic methods can overcome this issue but have not attracted much research interest in the past decades and are thus seldom applied in practice. The proposed multi-objective algorithm is an extension of the previously introduced deterministic single-objective Global Pattern Search algorithm. It achieves a thorough recovery of the Pareto frontier by tracking a predefined number of non-dominated samples during the optimisation run. To assess the numerical efficiency of the proposed method, it is compared to the well-established NSGA2 algorithm. Convergence is demonstrated and the numerical performance of the proposed optimiser is discussed on the basis of several analytic test functions. Finally, the optimiser is applied to two structural dynamics problems: transfer function estimation and finite element model updating. The introduced algorithm performs well on test functions and robustly converges on the considered practical engineering problems. Hence, this deterministic algorithm can be a viable and beneficial alternative to random-number-based approaches in multi-objective engineering optimisation

MOGPS Supplementary Material

Additional analytical test functions optimised using Multi-Objective Global Pattern Search

Data-driven vibration prognosis using multiple-input finite impulse response filters and application to railway-induced vibration of timber buildings

With this paper, we present a vibration prognosis method based on finite impulse responses. The impulse responses are identified using measurement data from an existing building and consider a multiple-input/multiple-output topology. Vibration prognosis in urban buildings is becoming increasingly important, since more and more buildings are being constructed close to urban infrastructure. Combined with modern and ecological choices of building materials and the low vibration levels required by current standards, serviceability in terms of structural dynamics becomes an issue. Sources of vibration in urban settings include railway and metro lines as well as road traffic. This work focuses on a method especially suited to the three- dimensional vibration state encountered in modern timber buildings. Under the assumption of linear time-invariant structural dynamic behaviour, we develop a time- domain identification approach. The novelties of this contribution lie in the formulation of a numerically efficient method to identify multiple-input finite impulse response filters and its application to measurement data of a timber building. We validate this data-driven prognosis method using measurement data from a building constructed from cross-laminated timber, considering the three-dimensional vibration behaviour. The accuracy and limitations are assessed using railway-induced vibrations as a typical source of disturbance by infrastructure. We show that vibration data from the foundation can be used for effective prognosis of the top floor slabs considering train types not included in the identification data set. Based on the prognosis method, a virtual sensor concept for long-term monitoring is presented

A new open-database benchmark structure for vibration-based Structural Health Monitoring

Vibration-based Structural Health Monitoring is an ongoing field of research in many engineering disciplines. As for civil engineering, plenty of experimental structures have been erected in the past decades, both under laboratory and real-life conditions. Some of these facilities became a benchmark for different kinds of methods associated with Structural Health Monitoring such as damage analysis and Operational Modal Analysis, which led to fruitful developments in the global research community. When it comes to the continuous monitoring and assessment of the structural integrity of mechanical systems exposed to environmental and operational variability, the robustness and adaptability of the applied methods is of utmost importance. Such properties cannot be fully evaluated under laboratory conditions, which highlights the necessity of outdoor measurement campaigns. To this end, we introduce a test facility for Structural Health Monitoring comprising a lattice tower exposed to realistic conditions and featuring multiple reversible damage mechanisms. The structure located near Hanover in Northern Germany is densely equipped with sensors to capture the structural dynamics. The environmental conditions are monitored in parallel. The obtained continuous measurement data can be accessed online in an open repository. That is the foundation for benchmarks, consisting of a growing data set that enables the development, evaluation, and comparison of Structural Health Monitoring strategies and methods. In this article, we offer a documentation of the test facility and the data acquisition system. Lastly, we characterize the structural dynamics with the help of a finite element model and by analyzing several month of data

Bayesian operational modal analysis of closely spaced modes for monitoring wind turbines

In this study, the applicability of Bayesian operational modal analysis (BAYOMA) to an operating onshore concrete-steel hybrid wind turbine tower is investigated. The results of the identification then provide reliable parameters for the structural health monitoring (SHM) of the tower. In the context of wind turbines, typical assumptions of linear time-invariant OMA methods are violated, so the validity of the identification uncertainties of BAYOMA is not necessarily given. In addition, closely spaced modes occur, for which the mode shape in particular is subject to high uncertainty. It can be stated, that the main part of the mode shape uncertainty corresponds to the alignment of these in the mode subspace. Due of these challenges, mode shapes are generally not taken into account when monitoring wind turbine towers. In order to include the mode shape in SHM scheme, the second-order modal assurance criterion (S2MAC) is applied in this study. This metric is able to eliminate the alignment uncertainty by comparing the mode shape with a mode subspace. Besides mode shapes, the reliability of natural frequencies and damping can also be better quantified by knowing the identification uncertainty. This finally enables a well-founded selection of suitable monitoring parameters for the future application of SHM for wind turbines. Preprint submitted to Engineering Structures

Influence of system changes on closely spaced modes of a large-scale concrete tower for the application to structural health monitoring

Concrete steel towers are increasingly being used for onshore wind turbines. The lower part consists of separated segmented concrete rings connected with dry joints. Due to slight deviations from the axisymmetric cross section, closely spaced modes occur. Therefore, the influences of small system changes on closely spaced modes, particularly the mode shapes, should be investigated to enable reliable vibration based monitoring. In this context, the influence of imperfections due to the waviness of the dry joints requires attention. As no acceleration measurements on concrete towers considering small system changes have been performed so far, this has not yet been investigated. Therefore, an experiment is carried out using a large-scale laboratory model of a prestressed concrete segment tower. The system modifications are introduced by changing the preload. This changes the influence of imperfections of the surfaces of the horizontal dry joints, estimated by measuring strain and displacement at the lowest joint. An increasing preload causes the first two pairs of bending modes to move closer together. This enables to study the effect of the closeness of natural frequencies on the related mode shapes based on the same structure. Thus, the known effects of increasing uncertainty of the alignment and a rotation of the mode shape in the mode subspace with closer natural frequencies can be shown experimentally. In this work the operational modal analysis (OMA) methods Bayesian-OMA (BAYOMA) and Stochastic Subspace Identification (SSI) are used. Local imperfections can significantly affect modal parameters, so these should be considered for vibration based monitorin