A self-adaptive method for the assessment of dynamic measurement uncertainty. (15th June 2022)

• Record Type: Journal Article
• Authors: Wang, Jun; Deng, Huaxia; Wu, Yimin; Ma, Mengchao; Zhong, Xiang
• Is Part Of: Measurement. Volume 196(2022)
• Publication Date: 2022-06-15
• Subjects: Self-adaptive -- Bayesian linear regression model -- Time-varying auto-regression model -- Dynamic measurement uncertainty; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2022.111116
• Languages: English
• ISSNs: 0263-2241

Abstract

Abstract: Measurement uncertainty is as important as measurement in metrology and industry. The GUM and its supplements provide a widely accepted framework for evaluating measurement uncertainty; but don't provide a reasonable assessment method for some special circumstances, especially for dynamic measurement. Several emerging methodologies with different mathematical approaches are used for evaluating the dynamic uncertainty in a specific application, such as knowing the characteristics of data. To expand the applicability, a self-adaptive method is proposed. This method evaluates measurement uncertainty by analyzing the compositions of dynamic data, regardless of linearity, stationarity, or stochasticity. Information entropy on spectra combined with EDM algorithms is presented to divide dynamic data into deterministic and stochastic components; and then a Bayesian model and a time-varying auto-regression model are used to analyze decomposed components, respectively. Synthetic noisy signals and experimental data from a double-rotor table are utilized to demonstrate the effectiveness of the proposed method. Highlights: A self-adaptive method for evaluating dynamic measurement uncertainty is developed. A criterion is presented to separate deterministic component from stochastic component. The developed method can be used to improve the precision of dynamic measurement.