Realizability Constrained Selection of Residual Generators for Fault Diagnosis with an Automotive Engine Application
This paper considers the problem of selecting a set of residual
generators for inclusion in a model-based diagnosis system, while
fulfilling fault isolability requirements and minimizing the number of
residual generators. Two novel algorithms for solving the selection
problem are proposed. The first algorithm provides an exact solution
fulfilling both requirements and is suitable for small problems. The
second algorithm, which constitutes the main contribution, is suitable
for large problems and provides an approximate solution by means of a
greedy heuristic and by relaxing the minimal cardinality requirement.
The foundation for the algorithms is a novel formulation of the
selection problem which enables an efficient reduction of the
search-space by taking into account realizability properties, with
respect to the considered residual generation method. Both algorithms
are general in the sense that they are aimed at supporting any
computerized residual generation method. In a case study the greedy
selection algorithm is successfully applied in an industrial sized
automotive engine system.
Carl Svärd, Mattias Nyberg and Erik Frisk
IEEE Transactions on Systems, Man, and Cybernetics: Systems,
2013
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