Assessing the
reliability of industrial machinery is an important aspect within maintenance
processes in order to maximize productivity and efficiency. In this paper we
propose to use graphical models for fault detection in industrial machinery
within a condition-based maintenance setting. The contribution of this work is
based on the hypothesis that during fault free operation the causal
relationships between the observed measurement channels are not changing.
Therefore, major changes in a graphical model might imply faulty changes within
the machine's functionality or its properties. We compare and evaluate four
methods for the identification of potential causal relationships on a real
world inspired use case. The results indicate that sparse models (using L1
regularization) perform better than traditional full models.