Measurement Uncertainties Physical Parameters and Calibration of Instruments /
This book fulfills the global need to evaluate measurement results along with the associated uncertainty. In the book, together with the details of uncertainty calculations for many physical parameters, probability distributions and their properties are discussed. Definitions of various terms are gi...
| Published in: | Springer eBooks |
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| Main Author: | |
| Corporate Author: | |
| Format: | eBook |
| Language: | English |
| Published: |
Berlin, Heidelberg :
Springer Berlin Heidelberg,
2012.
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| Subjects: | |
| Online Access: | http://dx.doi.org/10.1007/978-3-642-20989-5 Перейти в каталог НБ ТГУ |
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| 505 | 0 | |a Some Important Definitions -- Probability Functions -- Other Probability Functions -- Evaluation of Measurement Data -- Propagation of Errors/Uncertainty -- Uncertainty and Calibration of Instruments -- Calculation of Uncertainty -- Uncertainty in Calibration of a Surface Plate -- Uncertainty in Mass Measurement -- Uncertainty in Volumetric Measurement -- Uncertainty in Calibration of Some More Physical Instruments -- Uncertainty in Calibration of Electrical Instruments. | |
| 520 | |a This book fulfills the global need to evaluate measurement results along with the associated uncertainty. In the book, together with the details of uncertainty calculations for many physical parameters, probability distributions and their properties are discussed. Definitions of various terms are given and will help the practicing metrologists to grasp the subject. The book helps to establish international standards for the evaluation of the quality of raw data obtained from various laboratories for interpreting the results of various national metrology institutes in an international inter-comparisons. For the routine calibration of instruments, a new idea for the use of pooled variance is introduced. The uncertainty calculations are explained for (i) independent linear inputs, (ii) non-linear inputs and (iii) correlated inputs. The merits and limitations of the Guide to the Expression of Uncertainty in Measurement (GUM) are discussed. Monte Carlo methods for the derivation of the output distribution from the input distributions are introduced. The Bayesian alternative for calculation of expanded uncertainty is included. A large number of numerical examples is included. | ||
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