Abstract
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.
Published Version
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