We present in this article a case study of the probabilistic
approach to model-based diagnosis. Here, the diagnosed
system is a real-world electrical power system, namely the
Advanced Diagnostic and Prognostic Testbed (ADAPT) located
at the NASA Ames Research Center. Our probabilistic approach
is formally well-founded, and based on Bayesian networks and
arithmetic circuits. We pay special attention to meeting two of the
main challenges — model development and real-time reasoning
— often associated with real-world application of model-based
diagnosis technologies. To address the challenge of model development,
we develop a systematic approach to representing electrical
power systems as Bayesian networks, supported by an easy-touse
specication language. To address the real-time reasoning
challenge, we compile Bayesian networks into arithmetic circuits.
Arithmetic circuit evaluation supports real-time diagnosis by
being predictable and fast. In experiments with the ADAPT
Bayesian network, which contains 503 discrete nodes and 579
edges and produces accurate results, the time taken to compute
the most probable explanation using arithmetic circuits has a
mean of 0.2625 milliseconds and a standard deviation of 0.2028
milliseconds. In comparative experiments, we found that while
the variable elimination and join tree propagation algorithms
also perform very well in the ADAPT setting, arithmetic circuit
evaluation was an order of magnitude or more faster.

Reference:

O. J. Mengshoel, M. Chavira, K. Cascio, S. Poll, A. Darwiche,
and S. Uckun. "Probabilistic Model-Based Diagnosis: An Electrical
Power System Case Study”. Accepted to IEEE Transactions on
Systems, Man, and Cybernetics, Part A, 2009.