  Stochastic Closed-Loop Active Fault Diagnosis (CLAFD) aims to select the
input sequentially in order to improve the discrimination of different models
by minimizing the predicted error probability. As computation of these error
probabilities encompasses the evaluation of multidimensional probability
integrals, relaxation methods are of interest. This manuscript presents a new
method that allows to make an improved trade-off between three factors --
namely maximized accuracy of diagnosis, minimized number of consecutive
measurements to achieve that accuracy, and minimized computational effort per
time step -- with respect to the state-of-the-art. It relies on minimizing an
upper bound on the error probability, which is in the case of linear models
with Gaussian noise proven to be concave in the most challenging discrimination
conditions. A simulation study is conducted both for open-loop and feedback
controlled candidate models. The results demonstrate the favorable trade-off
using the new contributions in this manuscript.
