Kraemer, Peter
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Kraemer, Peter
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Publication Access status: Open Access , Automated set-up parameter estimation and result evaluation for SSI-Cov-OMATraditionally, modal analysis and the extraction of modal parameters from vibration data is a process that requires a more or less extensive amount of manual interaction from setting input parameters up until finding the eigenfrequencies. The growing interest in continuously monitoring mechanical structures e.g. for automated damage detection methods has led to the development of many approaches to automate different aspects of modal analysis. In this context, the Covariance-driven Stochastic subspace identification (Cov-SSI) is a widely used method. The present paper provides an automated Cov-SSI algorithm combined with a peak-picking approach for the automatic determination of input parameters. In this regard, using the Prominence-parameter allows to examine the PSD by finding the most relevant peaks. The herein shown algorithm is currently suitable for systems with a limited number of sensors. Cov-SSI results are arranged in stability plots and interpreted using the hierarchical clustering method. By creating stability plots for a wide range of block rows a sensitivity analysis is used to find the optimal result based on the averaged standard deviation of damping of the clusters in every stability plot. A second aspect of this paper is comparing the common method for order reduction with a modified method described in [1], which preserves the orthogonality of the , and matrix of the singular value decomposition. Exemplary results on both methods are provided using simulated data (state-space, 3 DoF)Source Type:Article4 9 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Open Access , Methods to enhance the automation of operational modal analysisThe vibration-based damage detection and the monitoring of modal data are currently based on different Operational Modal Analysis (OMA) approaches. For the continuous monitoring of modal quantities, different techniques for automated feature extraction are known. Especially in recent years several research groups and companies have been working on the automatic interpretation of stability plots. Nevertheless, many questions regarding data pre-processing for OMA in time or frequency domain are still unanswered. The present paper deals with issues regarding effective pre-processing methods for OMA based on Covariance-Stochastic Subspace Identification. In this context, the orthogonality of matrices after model order reduction, etc. are referred. This includes, for example, a comparison between the classical calculation of the reduced-order matrices and a procedure that preserves the orthogonality of these matrices. A method known from the signal denoising and image processing is also successful used to extract and select the modes. The mode extraction method is validated with an innovative three-dimensional stability plot. This paper does not claim to solve all tasks of an automated OMA, but it contributes the calculation of clean, easy to interpret, stability plots, which should facilitate the automatic evaluation in the future. The effectiveness of the algorithms is demonstrated by means of simulated (3DOF-StateSpace) and measured data of a laboratory structure described in [1]. Afterwards the results and the future works on the topic are discussed.Source Type:Article3 8 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Open Access , Novel methodologies for multiaxial strain measurements with piezoresistive films based on graphene nanoplateletsMany recent investigations in the context of graphene nanoplatelets (GNPs) coatings report surface strain measurements by using piezoresistive sensing capabilities. An often underestimated problem is that the strain field is unknown and the principal strain components as well as their orientations must be determined. Herein, GNP films subjected to multiaxial strain are examined. Experimental results show that although the sensitivity to longitudinal strain is the highest, the ratio between transverse and longitudinal sensitivity exceeds 0.5. The sensitivity to shear strain is much lower. A model assisted study of a random network provides additional guidelines for the different electromechanical sensitivities. In practice, the GNP film is usually subjected to different strains simultaneously so that the multiaxial strain measurement becomes difficult. Therefore, two novel approaches for sensing plane strain components with circular GNP films are developed and successfully verified in experiments. The numerical approach is called strain-differential electrical impedance tomography (SD-EIT), where the proposed piezoresistive model elementwise in a finite element model is implemented and the strain components of a strain rosette are reconstructed. Moreover, an analytical approach is derived from SD-EIT and exhibits further the opportunity to detect anomalies within the piezoresistive sensing behavior of GNP films.Source Type:Article1 13 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Open Access , Schadensdiagnoseverfahren für die Zustandsüberwachung von Offshore-Windenergieanlagen(2011)Vibration-based damage diagnosis is a promising approach for monitoring different structural and machine components of offshore wind energy plants. The aim of continuous monitoring is to reduce the inspection intervals, which especially offshore, are very time and cost consuming. At the same time an automatic damage diagnosis system provides the operator with necessary and reliable information about the plant's state, which allows the initiation of maintenance actions also if the plant is unreachable due to bad weather condition. This work is focused on the development of new methods which are included in one monitoring system performing damage diagnosis for different component of offshore wind energy plants, such as: tower, foundation and drive train. The development is based on the extension and combination of different signal analysis approaches in structural dynamics, pattern recognition and information theory. On this base a structural health monitoring concept dealing with measured data was developed and implemented. This includes structural damage detection algorithms in the presence of changing environmental and operational conditions, model-based damage localization by means of modal data and sensor fault identification. For condition monitoring purposes different approaches for signal analysis of measured time data belonging to the drive train components are extended and implemented. The applied algorithms combine different frequency domain approaches. These are used to detect damages in rolling bearings and planetary gears in an earlier stage compared to the established condition monitoring algorithms. The developed and applied methods in structural health monitoring are validated by means of simulation models, measured signals from different laboratory structures and measured data from a long time monitoring of the 5MW prototype wind energy plant built by Areva-Multibrid. The condition monitoring methods are validated by means of measured signals from different wind energy plants.Source Type:Doctoral ThesisSeries:Schriftenreihe der Arbeitsgruppe für Technische Mechanik im Institut für Mechanik und Regelungstechnik - MechatronikVolume number:31 5
