Methods for uncertainty evaluation in measurement | Statistical issues in metrology

Gianfranco Genta

ISBN 10: 3639280415 / ISBN 13: 9783639280418

Published by VDM Verlag Dr. Müller, 2010

About this Item

Methods for uncertainty evaluation in measurement | Statistical issues in metrology | Gianfranco Genta | Taschenbuch | Englisch | VDM Verlag Dr. Müller | EAN 9783639280418 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

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Bibliographic Details

• Title: Methods for uncertainty evaluation in measurement | Statistical issues in metrology
• Publisher: VDM Verlag Dr. Müller
• Publication Date: 2010
• Language: English
• ISBN 10: 3639280415
• ISBN 13: 9783639280418
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 7D millimeters 220 millimeters width by 150 millimeters height by 7 millimeters depth
• Weight: 192 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

This work is aimed at the study of different methods for uncertainty evaluation in measurement, both theoretical and applied. In the metrological field, the internationally accepted approach for uncertainty evaluation implies use of the GUM (Guide to the Expression of Uncertainty in Measurement). There are however situations in which it may not perform satisfactorily, e.g. when the measurement function is non-linear. The JCGM (Joint Committee for Guides in Metrology) has recently described in Supplement 1 to the GUM an alternative method for uncertainty evaluation, that is propagation of distributions using a Monte Carlo method. This method requires however an appropriate knowledge of statistical distribution of input quantities, a hard to meet requirement. It is possible to remedy resorting to the bootstrap method (Efron 1979), which does not require prior assumptions. This method has been employed as an alternative solution for uncertainty assessment of CMM (Coordinate Measuring Machines) and subjective evaluations. Uncertainty contributions may be influenced by the presence of outliers; some outlier detection methods have thus been considered.

About the Author

Research Fellow at the Department of Production Systems and Business Economics at Politecnico di Torino, where he received the M.Sc. degree in ?Mathematical Modelling in Engineering? and the Ph.D. degree in ?Metrology: Measuring Science and Technique?. His research is focused on statistical methods in metrology.