Bayesian Structural Learning for an Improved Diagnosis of Cyber-Physical Systems
This work addresses diagnosis issues in cyber-physical systems, providing a more interpretable and efficient solution, though it is incremental as it builds on existing methods.
The paper tackles the challenges of diagnosing cyber-physical systems, such as time-intensive model building and interpretability, by proposing a scalable algorithm that uses Bayesian networks and genetic optimization to learn structured models automatically. The result is a model with equal performance to state-of-the-art algorithms, offering better interpretability and reduced size.
The diagnosis of cyber-physical systems aims to detect faulty behaviour, its root cause and a mitigation or even prevention policy. Therefore, diagnosis relies on a representation of the system's functional and faulty behaviour combined with observations of the system taken at runtime. The main challenges are the time-intensive building of a model, possible state-explosion while searching for the root cause and interpretability of the results. In this paper we propose a scalable algorithm tackling these challenges. We use a Bayesian network to learn a structured model automatically and optimise the model by a genetic algorithm. Our approach differs from existing work in two aspects: instead of selecting features prior to the analysis we learn a global representation using all available information which is then transformed to a smaller, label-specific one and we focus on interpretability to facilitate repairs. The evaluation shows that our approach is able to learn a model with equal performance to state-of-the-art algorithms while giving better interpretability and having a reduced size.