Analyse großer Datenmengen in Verarbeitungsprozessen

The increasing complexity of industrial and agricultural manufacturing processes and the continuously growing amount of data have the impact that problems and faults in the processes are often detected too late. In this project, a self-learning assistance system is developed to cope with these challenges by automatic detection of failures, anomalies and the need for optimization. The developed assistance system accomplishes data acquisition, process monitoring and anomaly detection in industrial and agricultural manufacturing processes. Evaluation is conducted in three application cases: Large distillation columns, agricultural manufacturing processes and large-scaled sorting plants. In this paper, the developed infrastructures for data acquisition are described as well as the developed algorithms and initial evaluation results