In the process of realizing Industry 4.0 both equipment and industrial processes are digitally interconnected and great amounts of data are acquired. Machines continuously record their state or control variables and process steps are monitored by dedicated sensors. Data analytics are fundamental to gain a benefit from the dataset, but the sheer amount of data makes manual inspection almost impossible. Manually defined rules can be useful for a simple supervision of processes, but with complex data it is impossible to define every possibility. Only with automated software can the data be analyzed with feasible effort. Machine learning methods are therefore needed to exploit the full potential of the data. Analysis of time series data, which is ubiquitous in an industrial environment, is one of the key competences of the Machine Learning Enhanced Sensor Systems Group (MLS) of Fraunhofer EMFT.