Detection of anomalous patterns in streams of sensor data is an important challenge in today’s industries. Previously unseen patterns can be a sign of misconfiguration, increasing mechanical wear-out or simply unforeseen situations which may eventually lead to failures. Identification of anomalous patterns can facilitate the proactive detection of failures, as modern industrial settings rely extensively on sensor data for monitoring mechanical or electrical behaviour of devices. Currently, this process is often manually handled by engineers with advanced knowledge of the machinery in the specific domain. Industrial systems, however, often record massive amounts of sensor data, which makes reliable manual detection of anomalous samples impossible and immensely time-consuming.