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In this work, a method for vehicle load identification is proposed. It involves Bayesian
Analysis and (quasi-)static importance functions in order to estimate vehicle positions,
velocities and weights. The structure is modeled with finite elements in order to generate
model predictions for different load configurations. The model predictions are compared to
the actual measured data to identify the most probable loading configuration for that
measurement. This involves the use of enhanced Monte Carlo simulations such as MCMC to
reduce the computational effort.
Using digital twins for decision making is a very promising concept which combines simulation models with corresponding experimental sensor data in order to support maintenance decisions or to investigate the reliability. The quality of the prognosis strongly depends on both the data quality and the quality of the digital twin. The latter comprises both the modeling assumptions as well as the correct parameters of these models. This article discusses the challenges when applying this concept to realmeasurement data for a demonstrator bridge in the lab, including the data management, the iterative development of the simulation model as well as the identification/updating procedure using Bayesian inference with a potentially large number of parameters. The investigated scenarios include both the iterative identification of the structural model parameters as well as scenarios related to a damage identification. In addition, the article aims at providing all models and data in a reproducibleway such that other researcher can use this setup to validate their methodologies.
Following a modular concept an integrated monitoring system has been developed that includes all components of offshore wind turbines (OWEC). Using commercially available measurement equipments of the involved partners this system was tested at the wind turbine Multibrid M5000_2 in Bremerhaven. Additional results of the research project are findings on the use of embedded FBG sensors in rotor blades that allow condition assessments based on local information. Newly developed algorithms for the identification of external loads and damage characteristics of the structural components have been tested in operation. Developed methods for monitoring based structural assessment were used, which will be suitable to optimize structural maintenance works.