FlexDx: A Reconfigurable Diagnosis Framework
Detecting and isolating multiple faults is a computationally intense
task which typically consists of computing a set of tests, and then
computing the diagnoses based on the test results. This paper
describes FlexDx, a reconfigurable diagnosis framework which reduces
the computational burden by only running the tests that are currently
needed. The method selects tests such that the isolation performance
of the diagnostic system is maintained. Special attention is given to
the practical issues introduced by a reconfigurable diagnosis
framework such as FlexDx. For example, tests are added and removed
dynamically, tests are partially performed on historic data, and
synchronous and asynchronous processing are combined. To handle these
issues FlexDx uses DyKnow, a stream-based knowledge processing
middleware framework. The approach is exemplified on a relatively
small dynamical system, which still illustrates the computational gain
with the proposed approach.
Fredrik Heintz, Mattias Krysander, Jacob Roll and Erik Frisk
19th International Workshop on Principles of Diagnosis (DX-08),
2008
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