A Model of Uncertainty Quantification in the Estimation of Noise-Contaminated Transmissibility Measurements for System Identification
System identification via techniques applied in the frequency domain is a very common activity across many applications in engineering. Among many forms of frequency domain system identification, transmissibility estimation has been regarded as one of the most practical tools for its clear physical interpretation, its compatibility with output-only data, and its sensitivity to structural parameters. Due to operational variability, estimation errors, and extraneous noise, the computation of transmissibility may contain significant uncertainty, and this will affect the system identification quality. In this paper, a probability density function for transmissibility estimates is derived analytically via a Chi-square bivariate approach, and validated with Monte Carlo simulation.
KeywordsFrequency Response Function Structural Health Monitoring Extraneous Noise Clear Physical Interpretation Operate Deflection Shape
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