Data comparisons and uncertainty: a roadmap for gaining in competence and improving the reliability of results
| Autor: | Abdérafi Charki, Franco Pavese |
|---|---|
| Přispěvatelé: | Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS), Université d'Angers (UA), Istituto Nazionale di Ricerca Metrologica (INRiM) |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Technology
Computer science competence comparisons 02 engineering and technology 01 natural sciences Quality of results 010309 optics 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Proficiency testing Safety Risk Reliability and Quality Competence (human resources) compétence Accreditation [PHYS]Physics [physics] proficiency testing 020208 electrical & electronic engineering Uncertainty ISO/IEC 17025 Standard metrology reliability of results Systems engineering Measurement uncertainty laboratory |
| Zdroj: | International Journal of Metrology and Quality Engineering International Journal of Metrology and Quality Engineering, EDP sciences, 2019, 10, pp.1. ⟨10.1051/ijmqe/2018016⟩ International Journal of Metrology and Quality Engineering, Vol 10, p 1 (2019) |
| ISSN: | 2107-6839 2107-6847 |
| Popis: | International audience; This paper traces a roadmap for gaining in competence and for improving the reliability of results in a laboratory. The roadmap was built from the requirements concerning the results quality and measurement uncertainty, which accreditation bodies use for the accreditation of testing and calibration laboratories. In industry, accreditation is the accepted proof of a laboratory's assigned level of competence. The level of performance of a laboratory is demonstrated through the quality of its management of test and calibration results. Inter-laboratory comparisons and the evaluation of measurement uncertainties are recommended as the most appropriate methods for demonstrating continuous improvement in laboratories. The common methods used for data comparisons and for the evaluation of measurement uncertainties are highlighted. An overview of the main indicators used in data comparisons is presented. Some recommendations are made that are useful to the design of a roadmap for gaining in competence and for improving the quality of results obtained by a laboratory. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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