On-the-go throughput prediction in a combine harvester using sensor fusion

Dan Hermann, Morten L. Bilde, Nils Axel Andersen, Ole Ravn

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

The paper addresses design of a clean grain throughput observer for a combine harvester, i.e. delay free yield sensing. The aim is to predict grain throughput changes using the forward speed and a throughput sensor in the feederhouse. By utilising a grain flow model and sensor fusion an estimate of the current grain throughput is obtained, hence the effect from the lag in the momentary yield sensor reading due to material transport delays can be reduced. Statistical change detection is used to detect feederhouse load condition as well as sensor discrepancies using the observer innovation signal. The system is able to predict changes originating from forward speed and local crop density variations. Also temporary sensor discrepancies are detected and compensated in the grain flow estimate.
Original languageEnglish
Title of host publicationProceedings of the 2017 IEEE Conference on Control Technology and Applications
PublisherIEEE
Publication date2017
Pages67-72
ISBN (Print)9781509021826
DOIs
Publication statusPublished - 2017
Event2017 IEEE Conference on Control Technology and Applications - he Mauna Lani Bay Hotel and Bungalows Kohala Coast, Kohala Coast, United States
Duration: 27 Aug 201730 Aug 2017

Conference

Conference2017 IEEE Conference on Control Technology and Applications
Locationhe Mauna Lani Bay Hotel and Bungalows Kohala Coast
CountryUnited States
CityKohala Coast
Period27/08/201730/08/2017
Series2017 Ieee Conference on Control Technology and Applications (ccta)

Cite this

Hermann, D., Bilde, M. L., Andersen, N. A., & Ravn, O. (2017). On-the-go throughput prediction in a combine harvester using sensor fusion. In Proceedings of the 2017 IEEE Conference on Control Technology and Applications (pp. 67-72). IEEE. 2017 Ieee Conference on Control Technology and Applications (ccta) https://doi.org/10.1109/CCTA.2017.8062442