Latin Abstract :
A new method to generate optimal structured residuals from partial principal component models is introduced. The models are
selected according to a pre-designed fault-to-residual structure matrix. The structures are so chosen that a certain degree of freedom
is left to allow optimization. The performance measure for optimization is the ratio of the fault-gain to the noise standard deviation
in the residual; a max–min solution is sought that maximizes the smallest of these measures within a given residual structure. In the
special framework of partial PC models, the solution is obtained as a linear combination of the eigenvectors spanning the residual
space of the partial model. For the basic case, a two-dimensional residual space, there is a single continuous-valued optimization
parameter; with higher dimensional residual spaces the dimension of the optimization problem is growing as well. The new parametric
optimization is integrated with structural optimization, utilizing earlier results. Also, the basic static algorithm is extended
to discrete dynamic systems. In a simulation example, using data from an emulator of the Space-Shuttle main fuel tank, we demonstrated
one and two-dimensional searches (two and three-dimensional residual spaces) and compared them to the fixed, maximum-
zero residual design.
NaturalLanguageKeyword :
Fault diagnosis , Principal components , structured residuals , Optimal sensitivity
