abstract = "System identification is one of the most important
research directions. It is a diverse field which can be
employed in many different areas. One of them is the
model-based fault diagnosis. Thus, the problems of
system identification and fault diagnosis are closely
related. Unfortunately, in both the cases, the research
is strongly oriented towards linear systems, while the
problem of identification and fault diagnosis of
non-linear dynamic systems remains still open. There
are, of course, many more or less sophisticated
approaches to this problem, although they are not as
reliable and universal as those related to linear
systems, and the choice of the method to be used
depends on the application. The purpose of this paper
is to provide a new system identification framework
based on a genetic programming technique.Moreover, a
fault diagnosis scheme for non-linear systems is
proposed. In particular, a new fault detection observer
is presented , and the Lyapunov approach is used to
show that the proposed observer is convergent under
certain conditions. It is also shown how to use the
genetic programming technique to increase the
convergence rate of the observer. The final part of
this paper contains numerical examples concerning
identification of chosen parts of the evaporation
station at the Lublin Sugar Factory S.A., as well as
state estimation and fault diagnosis of an induction
motor.",
notes = "Evaporator and electrical induction motor Lubin Sugar
Factory November 1998 GP better than ARX (p1024)