Zusammenfassung
Process anomalies and unexpected failures of manufacturing systems are problems that cause a decreased quality of process and product. A better understanding of the system’s behavior with the aid of data is the key to improve reliability and process stability. Current data analytics approaches show decent results concerning the optimization of single processes but lack in extensibility to plants with high-dimensional data spaces. This paper presents and compares two data-driven self-learning approaches that are used to detect anomalies within large amounts of machine and process data. Models of the machine behavior are generated to capture complex interdependencies and to extract features that represent anomalies. The approaches are tested and evaluated on the basis of real industrial data from a metal forming process.
Nutzer
