Statistical Properties and Design Criterions for Fault Isolation in Noisy Systems
Fault diagnosis in the presence of noise and model errors is of
fundamental importance. In the paper, the meaning of fault isolation
performance is formalized by using the established notion of coverage
and false coverage from the field of statistics. Then formal relations
describing the relationship between fault isolation performance and
the residual related design parameters are derived. For small faults,
the measures coverage and false coverage are not applicable so
therefore, a different performance criteria, called sub-coverage, is
proposed. The performance of different AI-based fault isolation
schemes is evaluated and it is notably shown that the well known
principle of minimal cardinality diagnosis gives a bad
performance. Finally, some general design guidelines that guarantee
and maximize the fault isolation performance are proposed.
Mattias Krysander and Mattias Nyberg
19th International Workshop on Principles of Diagnosis (DX-08),
2008
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