  We present a general architecture for the monitoring and diagnosis of large
scale sensor-based systems with real time diagnostic constraints. This
architecture is multileveled, combining a single monitoring level based on
statistical methods with two model based diagnostic levels. At each level,
sources of uncertainty are identified, and integrated methodologies for
uncertainty management are developed. The general architecture was applied to
the monitoring and diagnosis of a specific nuclear physics detector at Lawrence
Berkeley National Laboratory that contained approximately 5000 components and
produced over 500 channels of output data. The general architecture is
scalable, and work is ongoing to apply it to detector systems one and two
orders of magnitude more complex.
