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Automated modal analysis for tracking structural change during construction and operation phases
(2019)
The automated modal analysis (AMA) technique has attracted significant interest over the last few years, because it can track variations in modal parameters and has the potential to detect structural changes. In this paper, an improved density-based spatial clustering of applications with noise (DBSCAN) is introduced to clean the abnormal poles in a stabilization diagram. Moreover, the optimal system model order is also discussed to obtain more stable poles. A numerical Simulation and a full-scale experiment of an arch bridge are carried out to validate the effectiveness of the proposed algorithm. Subsequently, the continuous dynamic monitoring system of the bridge and the proposed algorithm are implemented to track the structural changes during the construction phase. Finally, the artificial neural network (ANN) is used to remove the temperature effect on modal frequencies so that a health index can be constructed under operational conditions.
Das Bauwerk einer Windenergieanlage dient der Sicherstellung der Energieproduktion. Eine entsprechende Zuverlässigkeit der Struktur und deren permanente Bewertung und Unterhaltung ist dazu notwendig. In diesem Beitrag werden Möglichkeiten der Bewertung des für die Struktur wichtigen Schädigungsmechanismus Ermüdung sowie dessen Überwachung in Zusammenhang mit Schädigungsindikatoren aufgezeigt. Dabei wird auf strukturmechanische, werkstoffmechanische und probabilistische Modelle sowie auf Messdaten eines Prototyps einer OWEA zurückgegriffen. Ausgehend von der gesamtdynamischen Berechnung wird die Zuverlässigkeit gemäß der Auslegung für den Schädigungsmechanismus Ermüdung durch ein Wöhlerlinienmodell berechnet. Damit werden ermüdungssensible Komponenten der Struktur identifiziert. Schädigungsindikatoren zur Überwachung der Ermüdungsfestigkeit werden auf Grundlage schädigungs-äquivalenter Dehnungen vorgestellt.
Fatigue assessment of support structures for offshore wind energy converters.
The structure of wind energy converters secures a reliable energy production. Hence an adequate reliability of the structure and additionally permanent assessment and maintenance is required. Within this paper methods for the assessment of the important damage mechanism fatigue in combination with monitoring of the fatigue reliability utilizing damage indicators are proposed. These methods comprise structural system, material degradation and probabilistic models as well as monitoring data of an offshore wind energy converter prototype. The structural reliability of the structure as designed is calculated based on an overall dynamic analysis using a SN-approach. Herewith fatigue sensitive components are identified. Damage indicators for monitoring the fatigue reliability based on damage equivalent strains are presented.
This paper presents the development of a continuous dynamic monitoring System and its applications to different structures, with the purpose of understanding structural real behaviours under operational conditions and detecting early structural modifications. The first part of paper introduces a complete continuous dynamic System, consists of signal acquisition and communication, automated signal processing and management, investigation of the interaction between structures and its environmental/operational conditions, feature extraction and detection of structural modification.
The rest of paper describes the applications of continuous dynamic monitoring System to different structures such as a wind turbine System and a highway bridge.
To ensure a high operational reliability of future generations of offshore wind conversion systems (OWEC) with economically acceptable repair and maintenance efforts, comprehensive diagnosis and supervision concepts are required. Automatic monitoring systems will be an essential part of such concepts. Because of the fact, that during operation there will be static and dynamic interaction between the components 'structure', 'machinery' and 'blades' it is necessary to develop the monitoring techniques in an overall concept. These monitoring systems are supposed to be applied for the design and testing as well as for the operation and maintenance phases. The used methods are focused on the design requirements for the structure, which are stated in the respective standards and certification guidelines. For the validation of the design, methods are specified which would be also applicable for SHM in the operating condition, e.g. for the evaluation of the structural conditon possibly after the occurrence of damages or changes. The further task consists in the realization of condition monitoring for all components, aiming at early damage detection and the observation of the damage development and its evaluation. Here the task consists of determining with assessment procedures the optimum time for maintenance actions to be carried out.
In the context of national innovative project IMO-WIND, an integrated long term monitoring System was installed on a prototype of an offshore wind turbine System of 5 megawatt dass. The Federal Institute for Materials Research and Testing (BAM) was responsible for development of the SHM System. It consists of a signal acquisition System and a Signal processing and management System.
The signal acquisition System is composed of 14 acceleration sensors, 110 strain gauges and 4 inclination sensors, installed at the tripod foundation and the tower. It began to work in August 2007. In order to investigate the structural dynamic properties, extract efficient damage index and manage huge amounts of Vibration Signals and analysis results, an automated signal processing and management Software System is developed in LabVIEW® environment. It includes functions such as automated Operational Modal Analysis (OMA) on the basis of Stochastic Subspace Identification (SSI) method and poly-reference Least-Squares Complex Frequency Domain (p-LSCF) approach, investigation of environmental/operational effects on structural dynamic properties under operational conditions, features extraction using Principal Component Regression (PCR) and Multiple Linear Regression (MLR) as well as data/result management and visualization.
Besides, this paper also presents variations of structural modal parameters of a wind turbine system under complex environmental/operational conditions. Comparison of modal parameters automatically extracted by SSI method and poly-reference p-LSCF approach indicates that the latter method provides more stable modal identification results ffom the viewpoint of long term OMA. Subsequently, the influences of temperature, wind velocity, rotation speed and nacelle direction on modal parameters are generally discussed. Finally, variations of indentified modal parameters during two years are presented.