  The diagnosis of cyber-physical systems (CPS) is based on a representation of
functional and faulty behaviour which is combined with system observations
taken at runtime to detect faulty behaviour and reason for its root cause. In
this paper we propose a scalable algorithm for an automated learning of a
structured diagnosis model which -- although having a reduced size -- offers
equal performance to comparable algorithms while giving better
interpretability. This allows tackling challenges of diagnosing CPS:
automatically learning a diagnosis model even with hugely imbalanced data,
reducing the state-explosion problem when searching for a root cause, and an
easy interpretability of the results. Our approach differs from existing
methods in two aspects: firstly, we aim to learn a holistic global
representation which is then transformed to a smaller, label-specific
representation. Secondly, we focus on providing a highly interpretable model
for an easy verification of the model and to facilitate repairs. We evaluated
our approach on data sets relevant for our problem domain. The evaluation shows
that the algorithm overcomes the mentioned problems while returning a
comparable performance.
