Uncertainty laws of experimental modal analysis with known broadband input

• Main Authors: Ng, Ching-Tai, Wang, Peixiang, Au, Siu-Kui, Li, Binbin
• Other Authors: School of Civil and Environmental Engineering
• Format: Article
• Language: English
• Published: 2023
• Subjects: Engineering::Civil engineering; Forced Vibration Test; Uncertainty Laws; BAYOMA; Signal-to-Noise Ratio; Experimental Modal Analysis
• Online Access: https://hdl.handle.net/10356/171047
• Institution: Nanyang Technological University

‘Uncertainty law’ aims at closed-form asymptotic formulas for the relationship between the identification uncertainties of modal properties (e.g., natural frequency, damping ratio) and test configuration (e.g., noise level, number and location of sensors, data duration). Existing developments focused on the case of unknown-input (ambient), where it has been found that identification uncertainty does not vanish even for noiseless instruments, essentially because the input is unknown. A natural question is then on how the uncertainty depends on test configuration when the input is known, not to mention how the configuration should be quantified. Motivated by these and related questions, this paper develops the uncertainty laws of modal parameters for well-separated modes with known single broadband input, e.g., vibration test with a single shaker as in experimental modal analysis. Asymptotic expressions for the posterior coefficient of variation of modal parameters are derived via the Fisher Information Matrix for long data and small damping scenarios. Assumptions and theory are validated using synthetic and field test data. Governing factors motivated by the theory are investigated, including the equivalent modal signal-to-noise ratio (for known input), the number of measured degrees of freedom, shaker location, and data duration. By virtue of the Cramér-Rao bound in classical statistics, the developed uncertainty laws represent the lower bound of identification uncertainty with known broadband input that can be achieved by any unbiased estimator. They provide a scientific basis for planning and managing identification uncertainties in vibration tests with known input.