Detecting early enough the anomalous behavior of technical systems facilitates cost savings thanks to avoiding system
downtimes, guiding maintenance, or improving performance. The novel framework proposed in this paper processes event
streams originating from system monitoring for anomaly detection purposes. Therefore, statistical models characterizing
the normal behavior of the monitored system are learned from the events. Instead of having one coarse normal model for all
operational states, the proposed framework contains a mechanism for automatically detecting different conditions of the
system allowing for fine-tuned models for every condition. The performance of the framework is demonstrated by means of
a real-world application, where the log files of a large-scale printing machine are analyzed for anomalies.
AT – Automatisierungstechnik covers the entire field of automation technology. It presents the development of theoretical procedures and their possible applications. Topics include new discoveries about the development and application of methods. It presents the function, properties, and applications of tools and includes contributions from the worlds of research, academia, and industry.