Estimation of measurement uncertainty in a pressure gauge calibration process
DOI:
https://doi.org/10.14488/1676-1901.v24i3.5256Keywords:
Measurement uncertainty, Guide, Pressure gauge, ProbabilityAbstract
The knowledge of measurement uncertainty is an indispensable part of measurement results, being essential elements to ensure traceability and accuracy of manufacturing processes, as well as for their control and regulation. The Guide to the Expression of Uncertainty in Measurement (GUM) provides international guidelines for assessing measurement uncertainty, recognized in various fields as a measure of reliability and traceability. This article aims to determine the measurement uncertainty in a pressure gauge calibration process using the GUM method. The results indicate that the expanded uncertainties for the points of 30 kgf/cm², 45 kgf/cm², and 75 kgf/cm² of the pressure gauge range between 0.57062832 kgf/cm² and 0.637725568 kgf/cm², demonstrating a reliable range of values within which the true measured pressure may lie, with a coverage probability of 95.45%. Additionally, through the analysis of the results obtained, it was found that the uncertainty of the gauge resolution was the largest contribution to the expanded uncertainty at the three measurement points. These findings confirm the robustness of the methodology used in evaluating the uncertainty related to pressure measurements, thus establishing a solid foundation for future investigations.
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AGUILLARD, D. P. et al. Measurement of the positive muon anomalous magnetic moment to 0.20 ppm. Physical review letters, v. 131, n. 16, 2023.
ARENCIBIA, R. V.; RIBEIRO, J. R. DOS S. Incerteza na medição da largura de cordões de solda. Soldagem e Inspecao / Welding and Inspection, v. 14, n. 3, p. 263–269, 2009.
ASSUNÇÃO, W. et al. Contribuições para o cálculo da incerteza de medição por Simulação de Monte Carlo em um processo de calibração de Micrômetro.
Revista Contemporânea, v. 4, n. 7, p. e5087, 2024.
BICH, W.; COX, M. G.; HARRIS, P. M. Evolution of the ‘guide to the expression of uncertainty in measurement’. Metrologia, v. 43, n. 4, p. S161–S166, 2006.
CHAO, F. et al. A state-of-the-art review on uncertainty analysis of rotor systems. Mechanical systems and signal processing, v. 183, n. 109619, p. 109619, 2023.
CORAL, R. Propagação de Incertezas de Medição através de Redes Neurais Artificiais utilizando o Método de Monte Carlo. Revista Eletrônica Técnico Científica do IFSC, v.2, n. 1, p. 70-76, 2018.
COX, M. G. et al. Model-based measurement uncertainty evaluation, with applications in testing. Accreditation and quality assurance, v. 8, n. 12, p. 548–554, 2003.
COX, M.; HARRIS, P. The GUM and its planned supplemental guides. Accreditation and quality assurance, v. 8, n. 7–8, p. 375–379, 2003.
DA SILVA, E. L; MENEZES, E. M. T. Metodologia da pesquisa e elaboração de dissertação. UFSC, Florianópolis, 4ª edição, v. 123, 2005.
DE DEUS, A. D.; VACCARO, G. L. R. Uma abordagem para implementação de qualidade assegurada no fornecimento, baseada em análise de capacidade: um estudo de caso em uma empresa do setor automotivo. Revista produção online, v. 9, n. 4, 2009.
DONATELLI, G. D.; KONRATH, A. C. Simulação de Monte Carlo na avaliação de incertezas de medição. Revista de Ciência & Tecnologia. v. 13, n. 25, p. 5-15, 2005.
DUBÉ, L; PARÉ, G. Rigor in information systems positivist case research: Current practices, trends, and recommendations. MIS quarterly: management information systems, v. 27, n. 4, p. 597, 2003.
EISENHARDT, K. M. Building theories from case study research. Academy of management review, v. 14, n. 4, p. 532, 1989.
FAES, M.; MOENS, D. Recent trends in the modeling and quantification of non-probabilistic uncertainty. Archives of Computational Methods in Engineering. State of the Art Reviews, v. 27, n. 3, p. 633–671, 2020.
FORBES, A. B. An MCMC algorithm based on GUM Supplement 1 for uncertainty evaluation. Measurement: journal of the International Measurement Confederation, v. 45, n. 5, p. 1188–1199, 2012.
FRENKEL, R. B. Statistical background to the ISO guide to the expression of uncertainty in measurement. Technology transfer series monograph, National Measurement Institute of Australia, 2006.
GHEYSEN, L. et al. Uncertainty quantification o f the wall thickness and stiffness in an idealized dissected aorta. Journal of the mechanical behavior of biomedical materials, v. 151, p. 106370, 2024.
GUM – Guia para Expressão de Incertezas de Medições. Avaliação de dados de medição, 2008.
HAGAN, A.; COX, M. Simple informative prior distributions for Type A uncertainty evaluation in metrology. Metrologia, v. 60, n. 2, p. 025003, 2023.
HERNANDEZ, H. Probability distributions in groups of random elements. ForsChem Research Reports, v. 5, n.1, p. 1–1, 2020.
HOLAS, P.; KAMIŃSKA, J. Mindfulness meditation and psychedelics: potential synergies and commonalities. Pharmacological reports: PR, v. 75, n. 6, p. 1398–1409, 2023.
INCERPI, P.H. Incerteza de medição – método proposto para a análise da conformidade do produto. Dissertação (Mestrado em Engenharia de Produção). Universidade Federal de Itajubá, Itajubá, 2008.
INMETRO – Instituto Nacional de Metrologia, Qualidade e Tecnologia. A estimativa da incerteza de medição pelos métodos do ISO GUM 95 e de simulação de Monte Carlo, 2008.
JCGM. Evaluation of measurement data; Evaluation of measurement data An introduction to the “Guide to the expression of uncertainty in measurement” and related documents, 2009.
JURADO, K.; LUDVIGSON, S. C.; SERENA, N. G. Measuring uncertainty. American Economic Review, v. 105, n. 3, p. 1177–1216, 2015.
KACKER, R.; JONES, A. On use of Bayesian statistics to make theGuide to the Expression of Uncertainty in Measurementconsistent. Metrologia, v. 40, n. 5, p. 235–248, 2003.
KING, G. B. et al. Direct comparison between Bayesian and frequentist uncertainty quantification for nuclear reactions. Physical review letters, v. 122, n. 23, p. 232502, 2019.
KUSNANDAR, N. et al. Bibliometric review of measurement uncertainty: Research classification and future tendencies. Measurement: journal of the
International Measurement Confederation, v. 232, n. 114636, p. 114636, 2024.
LANDGRAF, W. R.; STEMPNIAK, C. R. Simulação de Monte Carlo e ferramentas para avaliação da incerteza de medição. In: Congresso Latino Americano de Metrologia, 2004.
LIRA, I. The generalized maximum entropy trapezoidal probability density function. Metrologia, v. 45, n. 4, p. L17–L20, 2008.
MAGALHÃES, F. W. Estimation of analytical measurement uncertainty for ethyl carbamate quantification in cachaça by CG-IDMS recalculated based on sound metrological and statistic concepts. Revista Virtual de Química, v. 13, n. 2, p. 394-418, 2021.
MARTINS, M. A. F. Contribuições Para a Avaliação da Incerteza de Medição No Regime Estacionário. Dissertação (Mestrado em Engenharia Industrial). Universidade Federal da Bahia, Salvador, 2010.
MARTINS, M. A. F. et al. Comparação entre os métodos linear e não linear para a avaliação da incerteza de medição. Controle & Automação, v. 21, n. 6, p. 557–576, 2010.
NEUDECKER, D. et al. Templates of expected measurement uncertainties. EPJ Nuclear sciences & technologies, v. 9, p. 35, 2023.
NOGUEIRA, R. et al. Development studies of a new metronidazole certified reference material. Journal of the Brazilian Chemical Society, v. 23, n.3, p. 435-444, 2012.
PANTEGHINI, M. Analytical performance specifications for combined uncertainty budget in the implementation of metrological traceability. Clinical chemistry and laboratory medicine, v. 62, n. 8, p. 1497–1504, 2024.
PSAROS, A. F. et al. Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons. Journal of computational physics, v. 477, n. 111902, p. 111902, 2023.
REARDEN, B. T. et al. Sensitivity and uncertainty analysis capabilities and data in SCALE. Nuclear technology, v. 174, n. 2, p. 236–288, 2011.
REIS, S. F. DA S. Quantificação de incertezas em método de aceleração para determinação da inércia de rotação. Trabalho de Conclusão de Curso. Universidade de Brasília, Brasília, 2017.
ROALD, L. A. et al. Power systems optimization under uncertainty: A review of methods and applications. Electric power systems research, v. 214, n. 108725, p. 108725, 2023.
ROBBI, D. B. et al. Quantificação de incertezas em experimento simples de vibrações. Revista Interdisciplinar De Pesquisa Em Engenharia. v. 1 n. 1, p. 1-25, 2015.
RODRIGUES, J. A. et al. Different approaches for estimation of the expanded uncertainty of an analytical method developed for determining pharmaceutical active compounds in wastewater using solid-phase extraction and a liquid chromatography coupled with tandem mass spectrometry method. Analytical methods: advancing methods and applications, v. 15, n. 1, p. 109–123, 2023.
SANTOS, D. R. DOS. Caracterização das fontes e expressão da incerteza de medição em processos de medições lineares do laboratório: UTFPR-LAMEC. Trabalho de Conclusão de Curso. Universidade Tecnológica Federal do Paraná, Curitiba, 2011.
SAXENA, R.; KUMAR, H. Design and development of trapezoidal-shaped transducer for industrial and scientific applications. Mapan, v. 39, n. 2, p. 349–363, 2024.
SEONI, S. et al. Application of uncertainty quantification to artificial intelligence in healthcare: A review of last decade (2013–2023). Computers in biology and medicine, v. 165, n. 107441, p. 107441, 2023.
VEEN, A. M. H. VAN DER; COX, M. G. Getting started with uncertainty evaluation using the Monte Carlo method in R. Accreditation and quality assurance, v. 26, n. 3, p. 129-141, 2021.
VIM – Vocabulário Internacional de Metrologia. Conceitos fundamentais e gerais e termos associados, 2012.
WHITE, D. R.; SAUNDERS, P. The propagation of uncertainty with calibration equations. Measurement science & technology, v. 18, n. 7, p. 2157–2169, 2007.
WILLINK, R. An inconsistency in uncertainty analysis relating to effective degrees of freedom. Metrologia, v. 45, n. 1, p. 63–67, 2008.
YIN, R. K. Estudo de caso: planejamento e métodos. Porto Alegre: Bookman, 2 ª edição, 2001.
ZHANG, J. Modern Monte Carlo methods for efficient uncertainty quantification and propagation: A survey. Wiley interdisciplinary reviews. Computational statistics, v. 13, n. 5, 2021.
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