  Sensor and control data of modern mechatronic systems are often available as
heterogeneous time series with different sampling rates and value ranges.
Suitable classification and regression methods from the field of supervised
machine learning already exist for predictive tasks, for example in the context
of condition monitoring, but their performance scales strongly with the number
of labeled training data. Their provision is often associated with high effort
in the form of person-hours or additional sensors. In this paper, we present a
method for unsupervised feature extraction using autoencoder networks that
specifically addresses the heterogeneous nature of the database and reduces the
amount of labeled training data required compared to existing methods. Three
public datasets of mechatronic systems from different application domains are
used to validate the results.
