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Type-A Worst-Case Uncertainty for Gaussian noise instruments

An analytical type-A approach is proposed for predicting the Worst-Case Uncertainty of a measurement system. In a set of independent observations of the same measurand, modelled as independent- and identically-distributed random variables, the upcoming extreme values (e.g. peaks) can be forecast by...

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Detalles Bibliográficos
Autores principales: Baccigalupi, Carlo, Arpaia, Pasquale, Martino, Michele
Lenguaje:eng
Publicado: 2015
Materias:
Acceso en línea:https://dx.doi.org/10.1088/1748-0221/10/07/P07007
http://cds.cern.ch/record/2058750
Descripción
Sumario:An analytical type-A approach is proposed for predicting the Worst-Case Uncertainty of a measurement system. In a set of independent observations of the same measurand, modelled as independent- and identically-distributed random variables, the upcoming extreme values (e.g. peaks) can be forecast by only characterizing the measurement system noise level, assumed to be white and Gaussian. Simulation and experimental results are presented to validate the model for a case study on the worst-case repeatability of a pulsed power supply for the klystron modulators of the Compact LInear Collider at CERN. The experimental validation highlights satisfying results for an acquisition system repeatable in the order of _25 ppm over a bandwidth of 5 MHz.