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Modified frequency and spatial domain decomposition method based on maximum likelihood estimation

机译:基于最大似然估计的修改频率和空间域分解方法

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摘要

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.
机译:在本研究中,使用仅输出的响应测量来开发用于模态参数识别的修改频率和空间域分解(MFSDD)技术。根据所呈现的过程,通过基于观察到的数据的最大似然估计来更新测量响应(输出PSD)的最可能的功率谱密度矩阵。与可用频域分解(FDD)技术不同,与测量噪声和建模误差相关联的预测误差项被包括在所提出的方法中。在这种情况下,从各个方面提供详细讨论,用于对参数估计质量的测量噪声和建模误差的影响。进行了两种数值和两个实验分析,以证明在一些极端效应下提出的方法的有效性和准确性。所获得的结果表明,在噪声测量的情况下,所提出的方法在模态参数估计中表现出非常好的性能。

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