  This paper investigates robust fault diagnosis of multiple air data sensor
faults in the presence of winds. The trade-off between robustness to winds and
sensitivity to faults is challenging due to simultaneous influence of winds and
latent faults on monitored sensors. Different from conventional residual
generators that do not consider any constraints, we propose a constrained
residual generator using moving horizon estimation. The main contribution is
improved fault sensitivity by exploiting known bounds on winds in residual
generation. By analyzing the Karush-Kuhn-Tucker conditions of the formulated
moving horizon estimation problem, it is shown that this improvement is
attributed to active inequality constraints caused by faults. When the
weighting matrices in the moving horizon estimation problem are tuned to
increase robustness to winds, its fault sensitivity does not simply decrease as
one would expect in conventional unconstrained residual generators. Instead,
its fault sensitivity increases when the fault is large enough to activate some
inequality constraints. This fault sensitivity improvement is not restricted to
this particular application, but can be achieved by any general moving horizon
estimation based residual generator. A high-fidelity Airbus simulator is used
to illustrate the advantage of our proposed approach in terms of fault
sensitivity.
