  Milling machines form an integral part of many industrial processing chains.
As a consequence, several machine learning based approaches for tool wear
detection have been proposed in recent years, yet these methods mostly deal
with standard milling machines, while machinery designed for more specialized
tasks has gained only limited attention so far. This paper demonstrates the
application of an acceleration sensor to allow for convenient condition
monitoring of such a special purpose machine, i.e. round seam milling machine.
We examine a variety of conditions including blade wear and blade breakage as
well as improper machine mounting or insufficient transmission belt tension. In
addition, we presents different approaches to supervised failure recognition
with limited amounts of training data. Hence, aside theoretical insights, our
analysis is of high, practical importance, since retrofitting older machines
with acceleration sensors and an on-edge classification setup comes at low cost
and effort, yet provides valuable insights into the state of the machine and
tools in particular and the production process in general.
