  The performance of fault detection filters relies on a high sensitivity to
faults and a low sensitivity to disturbances. The aim of this paper is to
develop an approach to directly shape these sensitivities, expressed in terms
of minimum and maximum singular values. The developed method offers an
alternative solution to the $\mathcal{H}_{-}/\mathcal{H}_{\infty}$ synthesis
problem, building upon traditional multiobjective synthesis results. The result
is an optimal filter synthesized via iterative convex optimization and the
approach is particularly useful for fault diagnosis as illustrated by a
numerical example.
