Monitoring a Complez Physical System using a Hybrid Dynamic Bayes Net

TL;DR

This paper presents a hybrid dynamic Bayesian network model for monitoring the complex Reverse Water Gas Shift system on Mars, addressing challenges like noise, nonlinearity, and multiple time scales, validated with real data.

Contribution

It introduces a novel hybrid DBN framework with techniques for nonlinear and multi-scale effects, specifically tailored for complex physical systems like RWGS.

Findings

Successful modeling of RWGS with real data

Effective handling of nonlinear behavior using numerical integration

Demonstrated feasibility of hybrid DBNs for real-world system monitoring

Abstract

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.