With the ongoing digitization of the manufacturing industry and the ability to bring together
data from manufacturing processes and quality measurements, there is enormous potential …

Anomalies represent deviations from the intended system operation and can lead to
decreased efficiency as well as partial or complete system failure. As the causes of …

Automating the monitoring of industrial processes has the potential to enhance efficiency
and optimize quality by promptly detecting abnormal events and thus facilitating timely …

Deep learning has greatly increased the capabilities of" intelligent" technical systems over
the last years [1]. This includes the industrial automation sector [1]-[4], where new data …

Digital Transformation integrates information technology across a broad spectrum of
industrial sectors. Industrial Cyber-Physical Systems (ICPS) play a vital role in this …

Deep learning-based predictive quality enables manufacturing companies to make data-
driven predictions of the quality of a produced product based on process data. A central …

Digital Twins have been described as beneficial in many areas, such as virtual
commissioning, fault prediction or reconfiguration planning. Equip** Digital Twins with …

Soft Sensors (SSs) are inferential dynamical models employed in industries to perform
prediction of process hard-to-measure variables based on their relation with easily …

Fault prediction based upon deep learning algorithms has great potential in industrial
automation: By automatically adapting to different usage contexts, it would greatly expand …

The early and robust detection of anomalies occurring in discrete manufacturing processes
allows operators to prevent harm, eg defects in production machinery or products. While …