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A Bayesian method for on-line evaluation of uncertainty in measurement of Coriolis flow meters

Par Conseil national de recherches du Canada

DOITrouver le DOI : https://doi.org/10.1016/j.measurement.2021.109448
AuteurRechercher : 1; Rechercher : ; Rechercher : 2
Affiliation
  1. Conseil national de recherches du Canada. Technologies numériques
  2. Conseil national de recherches du Canada. Énergie, les mines et l'environnement
FormatTexte, Article
SujetMeasurement uncertainty; clustering; Bayesian inference; Coriolis mass flow meters
Résumé
On-line evaluation of measurement uncertainty is crucial for process control and quality control in real applications. Traditional approaches to measurement uncertainty (MU) assume that measurands are repeated measurements collected in static laboratory conditions. On-line evaluation of MU, then, constitutes a challenging problem because the sensor data is collected under a variety of operating conditions. We propose a new method for the on-line evaluation of MU which consists of clustering time series data into groups with similar operational conditions and evaluating the MU using Bayesian inference. The mass count uncertainty measured using Coriolis mass flow meters on two hydrogen refueling stations is evaluated. The clustering of fueling events effectively reduces the process noise in on-line evaluation, and the Bayesian inference method identifies a much narrower uncertainty range than conventional methods. Therefore, our approach of using machine learning methods for on-line evaluation of MU is a promising practical approach.
Date de publication
Maison d’éditionElsevier
Dans
  • Measurement 179, 109448 (20 avril 2021).
Langueanglais
Publications évaluées par des pairsOui
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Identificateur de l’enregistrement920742ce-ca1b-4f26-9255-9d92a3d6b329
Enregistrement créé2021-06-11
Enregistrement modifié2021-06-23
Date de modification :