Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 11125-11131 |
| Seitenumfang | 7 |
| Fachzeitschrift | IFAC-PapersOnLine |
| Jahrgang | 53 |
| Ausgabenummer | 2 |
| Publikationsstatus | Veröffentlicht - 2020 |
| Veranstaltung | 21st IFAC World Congress 2020 - Berlin, Deutschland Dauer: 12 Juli 2020 → 17 Juli 2020 |
Abstract
Moving towards comprehensive digitalization of production facilities, it is critical to know the location of work pieces, charges, or other objects of interest that change location over time during production. For the case of a limited traceability of these objects, we first present a theoretical approach that performs a recursive Bayesian estimation of the object's location over time based on typical passage measurements in production (e. g. light barriers or RFID systems). The probabilistic method is based on a directed acyclic graph modeling the transfer and sojourn of the objects in the production network. Subsequently, the method is validated on simulated data while varying both size and measurement conditions of the process. The results show the benefit of the proposed method against a single estimation and demonstrate its potential for the application in real time scenarios.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Steuerungs- und Systemtechnik
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in: IFAC-PapersOnLine, Jahrgang 53, Nr. 2, 2020, S. 11125-11131.
Publikation: Beitrag in Fachzeitschrift › Konferenzaufsatz in Fachzeitschrift › Forschung › Peer-Review
}
TY - JOUR
T1 - Temporal Object Tracking in Large-Scale Production Facilities using Bayesian Estimation
AU - Kortmann, Karl Philipp
AU - Zumsande, Johannes
AU - Wielitzka, Mark
AU - Ortmaier, Tobias
PY - 2020
Y1 - 2020
N2 - Moving towards comprehensive digitalization of production facilities, it is critical to know the location of work pieces, charges, or other objects of interest that change location over time during production. For the case of a limited traceability of these objects, we first present a theoretical approach that performs a recursive Bayesian estimation of the object's location over time based on typical passage measurements in production (e. g. light barriers or RFID systems). The probabilistic method is based on a directed acyclic graph modeling the transfer and sojourn of the objects in the production network. Subsequently, the method is validated on simulated data while varying both size and measurement conditions of the process. The results show the benefit of the proposed method against a single estimation and demonstrate its potential for the application in real time scenarios.
AB - Moving towards comprehensive digitalization of production facilities, it is critical to know the location of work pieces, charges, or other objects of interest that change location over time during production. For the case of a limited traceability of these objects, we first present a theoretical approach that performs a recursive Bayesian estimation of the object's location over time based on typical passage measurements in production (e. g. light barriers or RFID systems). The probabilistic method is based on a directed acyclic graph modeling the transfer and sojourn of the objects in the production network. Subsequently, the method is validated on simulated data while varying both size and measurement conditions of the process. The results show the benefit of the proposed method against a single estimation and demonstrate its potential for the application in real time scenarios.
KW - Bayesian filter
KW - Directed graphs
KW - Probabilistic models
KW - Process models
KW - Production systems
KW - Recursive estimation
KW - Stochastic modeling
KW - Stochastic systems
UR - http://www.scopus.com/inward/record.url?scp=85105053906&partnerID=8YFLogxK
U2 - 10.1016/j.ifacol.2020.12.271
DO - 10.1016/j.ifacol.2020.12.271
M3 - Conference article
AN - SCOPUS:85105053906
VL - 53
SP - 11125
EP - 11131
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 -