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Analytical Model for Calibration Results Performances Enhancement, Resulting in Automated Prescription for Equipments
Technical Paper
2019-01-1878
ISSN: 0148-7191, e-ISSN: 2688-3627
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Event:
AeroTech Europe
Language:
English
Abstract
Most of the decisions taken every day are based on the results of measurements of all different events that occur around us. The reliability of these measurements depends basically on the environment in which they are carried out, the procedure defined and the equipment used, evaluating their different contributions through the uncertainty of measurement. In the case of the measuring equipment, the calibration process associated with adequate traceability provides part of the information necessary to contribute positively to the generation of reliability. However, the physical nature of the instruments means that all of them have a certain degree of drift in their metrological characteristics, which requires users to establish time intervals to confirm the maintenance of the goodness of measurement of such equipment. In this article, a methodological proposal for the processing of calibration data, which makes it possible to establish a systematic approach for the dynamic and flexible establishment of calibration intervals for measuring equipment in industrial environments, is introduced. Finally, the results of a practical experience with this methodology carried out in the Puerto Real plant of the company Airbus, supported by a computer application, are presented.
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Authors
Citation
Contreras, J., García Lasanta, J., Mendez-Huelva, D., and Garofano, J., "Analytical Model for Calibration Results Performances Enhancement, Resulting in Automated Prescription for Equipments," SAE Technical Paper 2019-01-1878, 2019, https://doi.org/10.4271/2019-01-1878.Also In
References
- UNE-EN ISO 14253-2 2012
- 2008
- CEM 2010
- 2011
- Kessel , W. Meßunsicherheit, ein wichtiges Element der Qualitätssicherung 1 PTB-Mitteilungen 108 377 382 1998
- Sanchez-Perez , A.-M. , de Vicente , J. , and Prieto , E. Errores, Incertidumbres y Evaluación de la Conformidad E-Medida. La Rev. Española Metrol. 2 94 103 2012
- Beamex 2007
- UNE-EN ISO/IEC 17025:2017 2017
- UNE-EN ISO 9001 2015
- ILAC-G24/OIML D 10 2007
- NCSL International 2010
- Bare , A. Simplified Calibration Interval Analysis NCSL Int. Work. Symp 1-7, 2006
- Wyatt , D.W. and Castrup , H.T. Managing Calibration Intervals NCSL Annual Workshop & Symposium Albuquerque 1-20, 1991
- Castrup , H.T. and Johnson , K.M. Techniques for Optimizing Calibration Intervals ASNE Test & Calibration Symposium Arlington 1-6, 1994
- Nunzi , E. , Panfilo , G. , Tavella , P. , Carbone , P. , and Petri , D. Stochastic and Reactive Methods for the Determination of Optimal Calibration Intervals IEEE Trans. Instrum. Meas. 54 4 1565 1569 2005 10.1109/TIM.2005.851501
- Macii , D. , Tavella , P. , Perone , E. , Carbone , P. , and Petri , D. Accuracy Comparison between Techniques for the Establishment of Calibration Intervals: Application to Atomic Clocks IEEE Trans. Instrum. Meas. 53 4 1167 1172 2004 10.1109/TIM.2004.830767
- Jackson , D.H. and Castrup , H.T. Reliability Analysis Methods for Calibration Intervals: Analysis of Type III Censored Data NCSL Workshop and Symposium Denver 1987
- Wang , J. , Zhang , Q. , and Jiang , W. Optimization of Calibration Intervals for Automatic Test Equipment Meas. J. Int. Meas. Confed. 103 87 92 2017 10.1016/j.measurement.2017.01.062
- Weise , K. and Woger , W. A Bayesian Theory of Measurement Uncertainty Meas. Sci. Technol. 3 1 1 11 1992 10.1088/0957-0233/4/1/001
- Lira , I. and Grientschnig , D. Bayesian Assessment of Uncertainty in Metrology: A Tutorial Metrologia 47 3 R1 R14 2010 10.1088/0026-1394/47/3/R01
- Elster , C. , Klauenberg , K. , Walzel , M. , Harris , P.M. , Cox , M.G. , Matthews , C. , Wright , L. , Allard , A. , Fischer , N. , Ellison , S. , Wilson , P. , Pennecchi , F. , Kok , G.J.P. , Veen , A. Van , der , and Pendrill , L. 2015
- Klauenberg , K. , Wübbeler , G. , Mickan , B. , Harris , P.M. , and Elster , C. A Tutorial on Bayesian Normal Linear Regression Metrologia 52 6 878 892 2015 10.1088/0026-1394/52/6/878
- Albert , J. Bayesian Computation with R New York Second, Springer-Verlag New York 2009 10.1007/978-0-387-92298-0 978-0-387-92297-3
- 2012
- ISO 6789-2 2017