Anomaly Detection on Real-World Industrial Manufacturing Applications with Additional Anomaly Type Clustering

Fabian Schoch, Pascal Graf, Tobias Schmieg, Nicholas Schloer, Carsten Wittenberg, Carsten Lanquillon, Nicolaj C. Stache

Abstract: Anomaly detection in time series data plays a crucial role in industrial manufacturing by identifying potential defects, inefficiencies, and equipment failures before they escalate into costly disruptions. With the increasing adoption of the Internet of Things (IoT), modern industrial systems generate vast amounts of sensor data, necessitating advanced detection methods. Deep learning (DL)-based anomaly detection has emerged as a state-of-the-art solution, but its practical deployment remains challenging.

External IDs:dblp:conf/hci/SchochGSSWLS25