Quantitative Stochastic Fault Diagnosability Analysis
A theory is developed for quantifying fault detectability
and fault isolability properties of static linear
stochastic models. Based on the model, a stochastic characterization
of system behavior in different fault modes is defined and
a general measure, based on the Kullback-Leibler information,
is proposed to quantify the difference between the modes. This
measure, called distinguishability, of the model is shown to
give sharp upper limits of the fault to noise ratios of residual
generators. Finally, a case-study of a diesel engine model shows
how the general framework can be applied to a dynamic and
nonlinear model.
Daniel Eriksson, Mattias Krysander and Erik Frisk
2011
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