Railway vehicles are generally maintained preventively
within certain time periods. Condition based predictive
maintenance strategies have a great economic potential so
that modern trains are equipped with many sensors in order
to perform diagnostics and prognostics of components.
Methods for fault detection need appropriate feature subsets
in order to achieve small in-sample and out-sample errors. In
our case the typical feature selection approach using pure
data-driven methods is difficult, as the number of possible
feature sets is very large. On the other hand there exists rich
domain knowledge and detailed physical models of the
mechanical system. The aim is to combine this knowledge
with the often used mathematical methods for feature
selection for improving classification of cases when a faulty
damper is present. Based on the dynamic equations of
motion, this paper presents heuristic feature selection via the
analysis of transfer functions. We describe several well-
known methods of automated feature selection and a
workflow which combines domain knowledge with
automated methods. Results show that it is difficult to define
features based only on domain-knowledge, but in
combination with data-driven techniques good classification
performance can be achieved.