HyDE Model-Based Diagnosis Engine for Stochastic Hybrid Systems

Model-based diagnosis deals with the problem of diagnosing faults in systems using a model of the system for guidance. This problem is complicated by the presence of hybrid dynamics in the system (continuous evolution of the system interspersed with discrete events like commands to change configuration), as well as uncertainties in the form of model approximations and sensor noise. Several model-based technologies have been developed and successfully demonstrated using discrete abstractions of the system as models. These techniques are severely restricted in model expressiveness due to the discrete nature of the models. Moreover, sophisticated model abstraction techniques, as well as algorithms to convert continuous data to discrete form, need to be developed for such an approach to work. Recently, there have been efforts to develop diagnostic engines for hybrid and stochastic systems. However, these techniques have either focused on parametric faults, or use a probabilistic approach to fault identification. Consistency-based approaches that have been successfully demonstrated using discrete models have not been extended to work with stochastic and hybrid models.