Bayesian analysis of a flow meter calibration problem

A turbine flow meter indicates the volume of fluid flowing through the device per unit of time. Such a flow meter is commonly calibrated at a few known flow rates over its measurement range. A calibration curve relating the pulse factor of the meter to the flow rate is then fitted to calibration dat...

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Bibliographic Details
Published in:Metrologia 2015-04, Vol.52 (2), p.392-399
Main Authors: Kok, G J P, van der Veen, A M H, Harris, P M, Smith, I M, Elster, C
Format: Article
Language:English
Subjects:
Bayesian analysis
Calibration
Comparative analysis
flow meter
Flow rate
Flowmeters
Knowledge
least squares
Least squares method
Measuring instruments
Metrology
posterior distribution
prior knowledge
regression
Uncertainty
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Summary:A turbine flow meter indicates the volume of fluid flowing through the device per unit of time. Such a flow meter is commonly calibrated at a few known flow rates over its measurement range. A calibration curve relating the pulse factor of the meter to the flow rate is then fitted to calibration data using an ordinary least squares approach. This approach does not consider prior knowledge that may exist about the flow meter or the calibration procedure. A Bayesian analysis enables prior knowledge to be taken into account. A Bayesian inference results in a posterior distribution for the unknown parameters of the calibration curve that may be seen as the most comprehensive uncertainty information about these unknowns. This paper investigates for a flow meter calibration problem the effects of appreciating prior knowledge on values of the calibration curve and their associated uncertainties. It presents the results of a Bayesian analysis and compares them to those obtained by an ordinary least squares approach.
ISSN:0026-1394
1681-7575
DOI:10.1088/0026-1394/52/2/392