Publication
Anomaly Detection in Sensor Data provided by Combine Harvesters
Ying Gu; Ansgar Bernardi; Thilo Steckel; Alexander Maier
In: INDIN-2016 - 14th International Conference on Industrial Informatics. IEEE International Conference on Industrial Informatics, Special Session #30 - Big Data, Advanced Analytics, and Knowledge Management in Manufacturing Ecosystems, located at INDIN-2016 - 14th International Conference on Industrial Informatics, July 19-21, Poitiers, France, IEEE, 2016.
Abstract
Modern combine harvesters often stay beyond their
theoretic optimal performance during harvesting operations.
Explanations and remedies for this reduced efficiency are difficult
to find, as actual performance is influenced by a variety of
different parameters. This paper presents a continuous analysis
of machine-provided data streams in order to assess potential reasons
(i.e. anomalies) and to identify suggestions for optimization.
To this end, new sensor data-based machine learning algorithms
are being developed, applied and evaluated.