Prognostics CoE Publications

Electrical power systems play a critical role in spacecraft
and aircraft, and they exhibit a rich variety of failure modes.
This paper discusses electrical power system fault diagnosis
by means of probabilistic techniques. Specically, we discuss
our development of a diagnostic capability for an electrical
power system testbed, ADAPT, located at NASA Ames.
We emphasize how we have tackled two challenges, regarding
modelling and real-time performance, often encountered
when developing diagnostic applications. We carefully discuss
our Bayesian network modeling approach for electrical
power systems. To achieve real-time performance, we build
on recent theoretically well-founded developments that compile
a Bayesian network into an arithmetic circuit. Arithmetic
circuits have low footprint and are optimized for embedded,
real-time systems such as spacecraft and aircraft.
We discuss our probabilistic diagnostic models developed for
ADAPT along with successful experimental results.