Details
| Original language | English |
|---|---|
| Pages (from-to) | 1261-1267 |
| Number of pages | 7 |
| Journal | IFAC-PapersOnLine |
| Volume | 53 |
| Issue number | 2 |
| Publication status | Published - 2020 |
| Event | 21st IFAC World Congress 2020 - Berlin, Germany Duration: 12 Jul 2020 → 17 Jul 2020 |
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.
Keywords
- Bayesian filter, Data fusion, Estimation algorithms, Probabilistic models, Stochastic approximation
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
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In: IFAC-PapersOnLine, Vol. 53, No. 2, 2020, p. 1261-1267.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Bayesian Fill Volume Estimation Based on Point Level Sensor Signals
AU - Zumsande, Johannes
AU - Kortmann, Karl Philipp
AU - Wielitzka, Mark
AU - Ortmaier, Tobias
N1 - Funding Information: This work is supported by the European Regional Development Fund grant ZW 6-85018381. We would like to thank our industrial partners Heinrich Meier Eisengie?erei GmbH & Co. KG, IAV GmbH and K?NKEL WAGNER Germany GmbH for their support.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Bayesian filter
KW - Data fusion
KW - Estimation algorithms
KW - Probabilistic models
KW - Stochastic approximation
UR - http://www.scopus.com/inward/record.url?scp=85105037276&partnerID=8YFLogxK
U2 - 10.1016/j.ifacol.2020.12.1852
DO - 10.1016/j.ifacol.2020.12.1852
M3 - Conference article
AN - SCOPUS:85105037276
VL - 53
SP - 1261
EP - 1267
JO - IFAC-PapersOnLine
JF - IFAC-PapersOnLine
SN - 2405-8963
IS - 2
T2 - 21st IFAC World Congress 2020
Y2 - 12 July 2020 through 17 July 2020
ER -