  Machine Learning (ML) has achieved great successes in recent decades, both in
research and in practice. In Cyber-Physical Systems (CPS), ML can for example
be used to optimize systems, to detect anomalies or to identify root causes of
system failures. However, existing algorithms suffer from two major drawbacks:
(i) They are hard to interpret by human experts. (ii) Transferring results from
one systems to another (similar) system is often a challenge. Concept learning,
or Representation Learning (RepL), is a solution to both of these drawbacks;
mimicking the human solution approach to explain-ability and transfer-ability:
By learning general concepts such as physical quantities or system states, the
model becomes interpretable by humans. Furthermore concepts on this abstract
level can normally be applied to a wide range of different systems. Modern ML
methods are already widely used in CPS, but concept learning and transfer
learning are hardly used so far. In this paper, we provide an overview of the
current state of research regarding methods for learning physical concepts in
time series data, which is the primary form of sensor data of CPS. We also
analyze the most important methods from the current state of the art using the
example of a three-tank system. Based on these concrete implementations1, we
discuss the advantages and disadvantages of the methods and show for which
purpose and under which conditions they can be used.
