A method for quantitative fault diagnosability analysis of stochastic linear descriptor models
Analyzing fault diagnosability performance for a given model, before
developing a diagnosis algorithm, can be used to answer questions like
``How difficult is it to detect a fault f_i?'' or ``How difficult is
it to isolate a fault f_i from a fault f_j?''. The main
contributions are the derivation of a measure, distinguishability, and
a method for analyzing fault diagnosability performance of
discrete-time descriptor models. The method, based on the
Kullback-Leibler divergence, utilizes a stochastic characterization of
the different fault modes to quantify diagnosability
performance. Another contribution is the relation between
distinguishability and the fault to noise ratio of residual
generators. It is also shown how to design residual generators with
maximum fault to noise ratio if the noise is assumed to be
i.i.d. Gaussian signals. Finally, the method is applied to a heavy
duty diesel engine model to exemplify how to analyze diagnosability
performance of non-linear dynamic models.
Daniel Eriksson, Erik Frisk and Mattias Krysander
Automatica,
2013
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