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Analysis of dynamic measurements

Working Group 8.42

Overview

Dynamic measurements can be found in many areas of metrology and industry such as, for instance, in the measurement of time-dependent forces or accelerations. Methods from signal processing are often applied in the analysis of dynamic measurements. In many applications linear time-invariant systems are appropriate to model dynamic measurements, where the output signal is obtained as a convolution of the input signal and the measurement system’s impulse response. Input and output signal are not proportional to each other, and estimation of the system’s input signal from its output signal constitutes one important task in the analysis of dynamic measurements. Often digital filters are employed for this purpose. The evaluation of the uncertainty associated with the estimated input signal is particularly important from a metrological perspective.

Typical dynamic measurement with time-dependent errors in the output signal caused by the dynamic behavior of the measurement system.

Typical examples are measurements of mechanical quantities as, for example, force, torque and pressure. Further examples are oscilloscope measurements for the characterization of high speed electronics, hydrophone measurements for the characterization of medical ultrasound devices, the spectral characterization of radiation sources, spectral color measurements and camera-aided temperature measurements.

 

Research

One focus of PTB‘s Working Group 8.42 is the development of methods for the estimation of the input signal from the output signal when the dynamic behavior of the measurement system has been characterized. This includes the development of procedures for the evaluation of the uncertainty associated with the estimated input signal. Another focus is the development of methods for the analysis of dynamic calibration measurements aimed at determining the dynamic behavior of a measurement system.

Software

Publications

Publication single view

Article

Title: Linear Mixed Models: Gum and Beyond
Author(s): B. Arendacká, A. Täubner, S. Eichstädt, T. Bruns and C. Elster
Journal: Measurement Science Review
Year: 2014
Volume: 14
Issue: 2
Pages: 52-61
DOI: 10.2478/msr-2014-0009
ISSN: 1335-8871
File URL: fileadmin/internet/fachabteilungen/abteilung_8/8.4_mathematische_modellierung/Publikationen_8.4/epjconf_icm2014_00003.pdf
Web URL: http://www.degruyter.com/view/j/msr.2014.14.issue-2/msr-2014-0009/msr-2014-0009.xml
Keywords: dynamic measurement, acceleration, dynamic calibration, mixed model, design of experiment
Tags: 8.42, Dynamik, Unsicherheit
Abstract: In Annex H.5, the Guide to the Evaluation of Uncertainty in Measurement (GUM) [1] recognizes the necessity to analyze certain types of experiments by applying random effects ANOVA models. These belong to the more general family of linear mixed models that we focus on in the current paper. Extending the short introduction provided by the GUM, our aim is to show that the more general, linear mixed models cover a wider range of situations occurring in practice and can be beneficial when employed in data analysis of long-term repeated experiments. Namely, we point out their potential as an aid in establishing an uncertainty budget and as means for gaining more insight into the measurement process. We also comment on computational issues and to make the explanations less abstract, we illustrate all the concepts with the help of a measurement campaign conducted in order to challenge the uncertainty budget in calibration of accelerometers.

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