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Modified frequency and spatial domain decomposition method based on maximum likelihood estimation
Highlights A modified Frequency and Spatial Domain Decomposition method is presented. An analytical spectral density matrix is obtained based on the maximum likelihood estimation. Effect of measurement noise and modelling errors are investigated. The proposed method is verified by numerical and experimental examples.
Abstract In this study, a Modified Frequency and Spatial Domain Decomposition (MFSDD) technique is developed for modal parameter identification, using output-only response measurements. According to the presented procedure, the most probable power spectral density matrix of the measured response (output PSD) is updated by a maximum likelihood estimation based on the observed data. Different from the available Frequency Domain Decomposition (FDD) techniques, a prediction error term which is associated with the measurement noise and modelling errors is included in the proposed methodology. In this context, a detailed discussion is provided from various aspects for the effect of measurement noise and modelling errors on the parameter estimation quality. Two numerical and two experimental analysis are conducted in order to demonstrate the effectiveness and accuracy of the proposed methodology under some extreme effects. The obtained results indicate that the proposed method shows very good performance in modal parameter estimation in case of noisy measurements.
Modified frequency and spatial domain decomposition method based on maximum likelihood estimation
Highlights A modified Frequency and Spatial Domain Decomposition method is presented. An analytical spectral density matrix is obtained based on the maximum likelihood estimation. Effect of measurement noise and modelling errors are investigated. The proposed method is verified by numerical and experimental examples.
Abstract In this study, a Modified Frequency and Spatial Domain Decomposition (MFSDD) technique is developed for modal parameter identification, using output-only response measurements. According to the presented procedure, the most probable power spectral density matrix of the measured response (output PSD) is updated by a maximum likelihood estimation based on the observed data. Different from the available Frequency Domain Decomposition (FDD) techniques, a prediction error term which is associated with the measurement noise and modelling errors is included in the proposed methodology. In this context, a detailed discussion is provided from various aspects for the effect of measurement noise and modelling errors on the parameter estimation quality. Two numerical and two experimental analysis are conducted in order to demonstrate the effectiveness and accuracy of the proposed methodology under some extreme effects. The obtained results indicate that the proposed method shows very good performance in modal parameter estimation in case of noisy measurements.
Modified frequency and spatial domain decomposition method based on maximum likelihood estimation
Hızal, Çağlayan (author)
Engineering Structures ; 224
2020-06-23
Article (Journal)
Electronic Resource
English
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