A General Framework for Model Based Diagnosis Based on Statistical Hypothesis Testing (revised version)
A framework for fault diagnosis, called \emph{structured hypothesis
tests}, is presented. It has earlier been developed within the
area of automatic control, but is in fact very much inspired by the
ideas developed in the AI area. The motivation was originally to
handle dynamic systems with noise. However, it is here shown that
also the noise-free case can be perfectly handled.
The system to be diagnosed, and also the different faults, are
described by differential equations, algebraic equations, and
probability distribution functions. By using the framework, it is
in the isolation possible to utilize all such modeled knowledge
about the faults. The diagnosis system is constructed by combining
a set of different hypothesis tests. In this way, the task of
diagnosis is transferred to the task of validating a set of
different models with respect to the measured data.
Mattias Nyberg
12:th International Workshop on Principles of Diagnosis,
2001
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