Anomaly detection in discrete manufacturing using self-learning approaches

Process anomalies and unexpected failures of manufacturing systems are problems that cause a decreased quality of process and product. 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...