Outlier detection is one of the most widely used technique to identify abnormal behavior in raw data. The sense of abnormal deviation mentioned here accounts not only for human made or system errors that naturally occur as part of the data but also as seldomly occuring events. In this paper, we propose a new algorithm called Grid Based Outlier Detection (GBOD) to find the hidden outliers in large data sets. In contrast to existing grid based methods which are limited to only some statistical based approaches, the GBOD algorithm is raised with two alternations to figure out different range of outliers depending on the interest of the user. First, the number of points in a local grid cell is used to decide whether a point is an outlier or not. In a second step, this approach is extended to method that assigns an outlier score to each data point. The simple design makes this algorithm extremely efficient for large data sets.

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 the 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.