רון שטרן

Model-based diagnosis (MBD) is difficult to use in practice because it requires a model of the diagnosed system, which is often very hard to obtain. We explore theoretically how observing the system when it is in a normal state can provide information about the system that is sufficient to learn a partial system model that allows automated diagnosis. We analyze the number of observations needed to learn a model capable of finding faulty components in most cases. Then, we explore how knowing the system topology can help us to learn a useful model from the normal observations for settings in which many of the internal system variables cannot be observed. Unlike other data-driven methods, our learned model is safe, in the sense that subsystems identified as faulty are guaranteed to truly be faulty.