Bayesian Structural Learning for an Improved Diagnosis of Cyber-Physical Systems

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.