the algorithms detect anomalies reliably, the algorithms could also help to reduce the requirements in preventive maintenance.

With the recent decrease in the costs of sensors and the possibility to connect large monitoring environments with the industrial Internet of Things, large data streams are nowadays captured and can be accessed in real time even for high frequency data. The implementation of condition-based maintenance is currently mostly lacking the tools to automatically and reliably process such large quantities of information in real time. In fact, the handling of high frequency data faces very specific challenges, such as the variable length of the data streams and the fact that the fault signatures can appear at completely different temporal or spectral scales, for example over several hours of continuous operation monitored at several thousands or millions Hertz [@Valle2009Streaming].

The recent development of deep learning offers the possibility to automatically learn from large data sets the signatures that will maximise its training objective. Deep learning offers nowadays state-of-the art results in supervised setups. That is, in cases where the algorithm is trained with enough representative samples of all conditions to discriminate [@qu2019gear; @fawaz2019deep]. Anomaly detection, however, is a more challenging task since an anomaly 