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

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

Identification Uncertainties of Bending Modes of an Onshore Wind Turbine for Vibration-Based Monitoring

This study considers the identification uncertainties of closely spaced bending modes of an operating onshore concrete-steel hybrid wind turbine tower. The knowledge gained contributes to making mode shapes applicable to wind turbine tower monitoring rather than just mode tracking. One reason is that closely spaced modes make it difficult to determine reliable mode shapes for them. For example, the well-known covariance-driven stochastic subspace identification (SSI-COV) yields complex mode shapes with multiple mean phases in the complex plane, which does not allow error-free transformation to the real space. In contrast, the Bayesian Operational Modal Analysis (BAYOMA) allows the determination of real mode shapes. The application of BAYOMA presents a further challenge when quantifying the associated uncertainties, as the typical assumption of a linear, time-invariant system is violated. Therefore, validity is not self-evident and a comprehensive investigation and comparison of results is required. It has already been shown in a previous study that the significant part of the uncertainty in the mode shapes corresponds to their orientation in the mode subspace (MSS). Despite all the challenges mentioned, there is still a great need to develop reliable monitoring parameters (MPs) for Structural Health Monitoring (SHM). This study contributes to this by analysing metrics for comparing mode shapes. In addition to the well-known Modal Assurance Criteria (MAC), the Second-Order MAC (S2MAC) is also used to eliminate the alignment uncertainty by comparing the mode shape with a MSS. In addition, the mode shape identification uncertainties of BAYOMA are also considered. Including uncertainties is also essential for the typically used natural frequencies and damping ratios, which can be more appropriately used if the identification uncertainty is known

On using autoencoders with non-standardized time series data for damage localization

In this paper, an autoencoder trained with non-standardized time series data and evaluated using covariance-based residuals for generally applicable unsupervised damage localization is investigated. Raw acceleration time series are used as the inputs for the autoencoder to give both these features: no loss of information and exploitation of the full potential of the neural network. When it comes to output-only and unsupervised structural health monitoring (SHM), data-driven models struggle to localize the positions of damage adequately or only work well in a small range of applications. Regarding neural networks, expertise is needed for the neural network dimensioning and understanding of structural dynamics, which increases the difficulty of the task. In order to simplify the process, an automated solution is provided to perform the neural architecture search, and principal component analysis (PCA) is used to find a good choice for the bottleneck dimension. As an extension of the model, the residuals between the original and reconstructed time series are evaluated using the covariance between each input signal and each residual time series, which results in improved indicators for damage localization. We demonstrate the efficiency of the proposed schemes for damage analysis in a series of simulations using a three-mass swinger, in which the autoencoder can localize the damage using varying excitation locations. The covariances’ evaluation indicates that they are more potent than using the reconstruction error. Finally, experimental validation is conducted using vibration test data from a lattice tower called Leibniz University Structure for Monitoring (LUMO) under ambient excitation. For each damage pattern, high sensitivity towards local stiffness is achieved. Additionally, the damage position indicators exhibit a clear decreasing trend as the distance from the damage increases. The autoencoders presented here with non-standardized time series and covariance-based evaluation of residuals lead to increased robustness and sensitivity regarding damage localization

Heteroscedastic Gaussian processes for data normalisation in probabilistic novelty detection of a wind turbine

This study investigates the data normalisation of modal parameters of an operating concrete–steel hybrid onshore wind turbine tower considering also the identification uncertainty. In order to take into account the Environmental and Operational Condition (EOC)-dependent variance, sparse heteroscedastic Gaussian processes (GPs) are used for the data normalisation. Following a typical vibration-based Structural Health Monitoring (SHM) scheme, data normalisation of the natural frequencies and the mode shapes is performed first. Subsequently, a metric is defined which takes into account both the identification uncertainty and the operation-dependent uncertainty in order to enable novelty detection. The data normalisation methods must be able to handle uncertainties of different magnitudes due to EOCs in the data. In this context, GPs can be a suitable tool. However, standard GPs assume homoscedasticity, which is an unrealistic assumption in the case of EOC-dependent variance. Using a heteroscedastic GP instead, the variance of the data is better mapped and allows comparison with the identification uncertainties of Bayesian operational modal analysis (BAYOMA), taking into account the specifics of closely spaced modes of the tower structure. This leads to a better interpretation of the data and enables the introduction of a probabilistic novelty metric. This data normalisation approach, taking into account EOC-dependent uncertainties using heteroscedastic GPs, is being applied for the first time to a tower of a full scale 3.4 MW wind turbine in operation. Following this approach, it is possible to detect smaller changes in natural frequencies and second-order modal assurance criterion (S2MAC) compared to the assumption of homoscedasticity within the GP. In addition, a novelty was detected using the S2MAC during the period under study. Therefore, it can be illustrated that mode shape-based metrics tend to be more sensitive than purely frequency-based ones. However, it is difficult to assess the significance of such changes for structural integrity without further information