Distributed diagnosis in uncertain environments using Dynamic Bayesian Networks

Model-based diagnosis for industrial applications have to be efficient, and deal with modeling approximations and measurement noise. This paper presents a distributed diagnosis scheme, based on Dynamic Bayesian Networks (DBNs) that generates globally correct diagnosis results through local analysis, by only communicating a minimal number of measurements among diagnosers. We demonstrate experimentally that our distributed diagnosis scheme is computationally more efficient than its centralized counterpart, and it does not compromise the accuracy of the diagnosis results.