  The current development of today's production industry towards seamless
sensor-based monitoring is paving the way for concepts such as Predictive
Maintenance. By this means, the condition of plants and products in future
production lines will be continuously analyzed with the objective to predict
any kind of breakdown and trigger preventing actions proactively. Such
ambitious predictions are commonly performed with support of machine learning
algorithms. In this work, we utilize these algorithms to model complex systems,
such as production plants, by focusing on their variable interactions. The core
of this contribution is a sliding window based algorithm, designed to detect
changes of the identified interactions, which might indicate beginning
malfunctions in the context of a monitored production plant. Besides a detailed
description of the algorithm, we present results from experiments with a
synthetic dynamical system, simulating stable and drifting system behavior.
