  The Reverse Water Gas Shift system (RWGS) is a complex physical system
designed to produce oxygen from the carbon dioxide atmosphere on Mars. If sent
to Mars, it would operate without human supervision, thus requiring a reliable
automated system for monitoring and control. The RWGS presents many challenges
typical of real-world systems, including: noisy and biased sensors, nonlinear
behavior, effects that are manifested over different time granularities, and
unobservability of many important quantities. In this paper we model the RWGS
using a hybrid (discrete/continuous) Dynamic Bayesian Network (DBN), where the
state at each time slice contains 33 discrete and 184 continuous variables. We
show how the system state can be tracked using probabilistic inference over the
model. We discuss how to deal with the various challenges presented by the
RWGS, providing a suite of techniques that are likely to be useful in a wide
range of applications. In particular, we describe a general framework for
dealing with nonlinear behavior using numerical integration techniques,
extending the successful Unscented Filter. We also show how to use a
fixed-point computation to deal with effects that develop at different time
scales, specifically rapid changes occurring during slowly changing processes.
We test our model using real data collected from the RWGS, demonstrating the
feasibility of hybrid DBNs for monitoring complex real-world physical systems.
