Abstract
Dynamic measurement sets out to measure the variations in the values of a quantity over time [1–4]. It covers a wide application area, since it is very common that a quantity varies over time: examples are vibration, sound and electromagnetic radiations. Examples of application areas include the monitoring of continuous industrial processes, the sensing function in automatic control, vehicles guidance, experimental biomechanics, psychophysics and perception.
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Notes
- 1.
An internal model is one in which (internal) state variables appear in addition to input/output variables that solely appear in input–output models.
- 2.
It was acquired in the 1980s as a part of a collaboration of our Measurement Laboratory with Politecnico di Milano and ENEL (the Italian National Energy Board) [17].
References
Crenna, F., Michelini, R.C., Rossi, G.B.: Hierarchical intelligent measurement set-up for characterizing dynamical systems. Measurement 21, 91–106 (1997)
Morawski, R.Z.: Unified approach to measurand reconstruction. IEEE Trans. IM 43, 226–231 (1994)
Hessling, J.P.: Dynamic metrology. Measur. Sci. Technol. 19, 084008 (2008) (7p)
Sommer, K.D.: Modelling of measurements, system theory, and uncertanty evaluation. In: Pavese, F., Forbes, A. (eds.) Data Modeling for Metrology and Testing in Measurement Science, pp. 275–298. Birkhauser-Springer, Boston (2009)
Kwakernaak, H., Sivan, R.: Linear Optimal Control Systems. Wiley, New York (1972)
Oppenheim, A.V., Shafer, R.W.: Digital Signal Processing. Prentice Hall, Englewood Cliffs (1975)
Priestley, M.B.: Spectral Analysis and Time Series. Academic, London (1982)
Papoulis, A.: Probability, Random Variables and Stochastic Processes, 2nd edn. McGraw-Hill, Singapore (1984)
Marple, S.L.: Digital Spectral Analysis. Prentice-Hall, Englewood Cliffs (1987)
Kay, S.M.: Modern Spectral Estimation. Prentice Hall, Englewood Cliffs (1988)
Rossi, G.B.: A probabilistic model for measurement processes. Measurement 34, 85–99 (2003)
Rossi, G.B.: Measurement modelling: Foundations and probabilistic approach. Paper presented at the 14th joint internatinal IMEKO TC1+TC7+TC13 symposium, Jena, 31 August–2 September, 2011 (2011)
Aumala, O.: Fundamentals and trends of digital measurement. Measurement 26, 45–54 (1999)
Eichstädt, S., Elster, C., Esward, T.J., Hessling, J.P.: Deconvolution filters for the analysis of dynamic measurement processes: A tutorial. Metrologia 47, 522–533 (2010)
Bentley, J.P.: Principles of Measurement Systems, 4th edn. Pearson Education Ltd., Harlow (2005)
Larry Bretthorst, G.: Bayesian spectrum analysis and parameter estimation. Springer, New York (1988)
Diana, G. (ed.): Diagnostics of Rotating Machines in Power Plants. Springer, Berlin (1994)
Rossi, G.B.: Toward an interdisciplinary probabilistic theory of measurement. IEEE Trans. Instrum. Meas. 61, 2097–2106 (2012)
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Rossi, G.B. (2014). Dynamic Measurement. In: Measurement and Probability. Springer Series in Measurement Science and Technology. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-8825-0_12
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