Statistical Properties and Design Criterions for AI-Based Fault Isolation
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 shown that the well known principle of
minimal cardinality diagnosis gives a very bad performance. Finally,
some general design guidelines that guarantee and maximize the fault
isolation performance are proposed.
Mattias Nyberg and Mattias Krysander
IFAC World Congress,
2008
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