Bayesian Fill Volume Estimation Based on Point Level Sensor Signals

verfasst von

Johannes Zumsande, Karl Philipp Kortmann, Mark Wielitzka, Tobias Ortmaier

Abstract

In dry bulk and fluid processing, the composites are usually stored in hoppers, tanks, or other containers. Due to the economic advantages, binary point level sensors, which detect fill level exceeding, are widely used for process monitoring and control. In this paper, we propose different filters for estimating the probability distribution of the fill volume based on a time-variant measurement distribution and a stochastic physical model with white process noise. A filter based on the model prediction with separated measurement update and two Bayesian particle filters are proposed and compared with a simulated ground truth. The performance measures are the root-mean-square error, the precision of the 95 % and 75 % credible intervals, and the average value of the estimated probability density function at the simulated fill volumes.

Organisationseinheit(en)

Institut für Mechatronische Systeme

Typ

Konferenzaufsatz in Fachzeitschrift

Journal

IFAC-PapersOnLine

Band

53

Seiten

1261-1267

Anzahl der Seiten

7

ISSN

2405-8963

Publikationsdatum

2020

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Steuerungs- und Systemtechnik

Elektronische Version(en)

https://doi.org/10.1016/j.ifacol.2020.12.1852 (Zugang: Offen)