On validating propositional logic system descriptions for fault diagnosis
Alexander Diedrich, Lukas Moddemann, Oliver Niggemann · Engineering Applications of Artificial Intelligence · 2025
Correct and useful system descriptions are central to model-based fault diagnosis, as they describe the structure and behaviour of the system. But so far, system descriptions were always interpreted as complete propositional logic models and were assumed to be given. However, with increasing use of data-driven methods that approximate system descriptions, it cannot be guaranteed that the system description is a complete model of the system. It cannot even be guaranteed that the system description contains all observations, components, and connections that the real system exhibits. This requires novel approaches to determine how well a system description models the real system and how well it can thus be used for fault diagnosis. We present a novel algorithm which takes a syntactic approach to calculate diagnosability of approximated system descriptions. This is different from previous diagnosability research, which determined the diagnosability of real systems, where the algorithm could rely on the model’s completeness and corresponding reliable observations. With approximated models, observations and models are (partially) disconnected. We also present two novel algorithms to calculate new metrics that determine how close two approximated system descriptions are to each other by heuristically solving the graph alignment problem. We believe that our new approach and corresponding algorithms benefit practitioners who want to evaluate the quality of their approximated models for fault diagnosis. We show the usefulness of our results on established benchmarks of tank-systems, the Tennessee Eastman Process, two spacecraft, and a benchmark of different Boolean circuits.