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Managing agricultural processes with significant logistics sub-processes is a challenge because coordinating a distributed fleet in a dynamic environment is difficult without proper oversight in terms of qualitative and quantitative process information. Digital assistance systems are thought to aid agricultural practitioners by providing process-related information and thus support operational decision-making or even control the logistic flow (semi-)automatically. However, their development is currently stifled by a lack of monitoring capabilities during process execution. This thesis concerns the topic of online process monitoring for ongoing agricultural logistic processes. It discusses how to extract process knowledge from the telemetry of agricultural machines by applying spatio-semantic reasoning techniques. Our method combines spatial analysis for identifying spatial relationships between machines and their environment with semantic inference to derive formal process knowledge through ontological and rule-based reasoning. To test our concepts, we implemented a domain-agnostic semantic mapping framework and applied it in the context of forage maize harvesting. We present custom-made ontological models and rules to represent agricultural environments and to reason about machine actors and their process states. Based on our prototype, we demonstrate how to implement automated process and service tracking in near-real-time. Finally, we discuss the role of online process analytics systems in the context of other agricultural assistance systems for farm and fleet management.

The increasing amount of available data in production systems is associated with great potential for process optimization. Due to lack of a data analytics methodology and low data quality within production these potentials often remain unused. Therefore, in this paper we present a model for data acquisition and data preparation including feature engineering for characteristic sensor signals of production machines. The model allows the extraction of relevant process information from the signal, which can be used for monitoring, KPI tracking, trend analysis and anomaly detection. The approach is evaluated on an industrial turning process.

The process control of metalcutting production processes requires a knowledge of the spatial and temporal behaviour of the cutting forces. By including the clamping unit into he sensory means required for measuring such forces, it is possible to determine two process values from on sensor signal, namely the cutting and the clamping force. The measurement of forces by using strain control strips is based on the linear dynamic theory of deformation, so that it becomes possible to distinguish the static part of the signal from the dynamic one.

Die vorgelegte Arbeit zeigt neue und weiterentwickelten Methoden und Geräte für die Durchführung von kontinuierlichen Analysen und darauf basierenden Prozesssteuerungen. Sie basieren auf einem OLGA-System und eignen sich für die Überwachung chemischer Produktionsprozesse, Abwasser- und Schwermetallanalytik sowie zur Kontrolle von Weinfermentationen. Für die Weinanalytik wurden reagenzlose Biosensoren unter Verwendung des Enzyms Quinohemoprotein-Alkoholdehydrogenase entwickelt. Die Abscheidung von Enzymen und Redoxkomplexen durch lokalisierte pH-Modulation führte zu reagenzlosen Biosensoren, die durch ein geringes Arbeitspotential unanfällig gegenüber Interferenzen sind. Ein weiterer Teil dieser Arbeit beschreibt den Aufbau eines Gerätes für die automatische und parallele Durchführung von Elektrosynthesen und elektroanalytischen Untersuchungen in Mikrotiterplatten. Die Steuerung über eine Skriptsprache ermöglicht die Kombination beliebiger elektrochemischer und mechanischer Vorgänge.

Die erste Chemnitzer Brennstoffzellenkonferenz wurde vom Innovationscluster HZwo und dem Fraunhofer-Institut für Werkzeugmaschinen und Umformtechnik IWU durchgeführt. Ausgewählte Fachbeiträge und Präsentationen werden in Form eines Tagungsbandes veröffentlicht. The first fuel cell conference was initiated by the innovation cluster HZwo and the Fraunhofer Institute for Machine Tools and Forming Technology. Selected lectures and presentations are published in the conference proceedings.