Thomas Parisini, Professor
Danieli Endowed Chair of Automation Engineering
Dept. of Electrical, Electronic and Computer Engineering
University of Trieste
Via Valerio 10, 34127 Trieste, Italy
The objective of this lecture is give a tutorial and detailed overview of a
recent approach to the solution of the challenging problem of fault
detection and isolation and of a class of nonlinear uncertain systems. This
methodology is based on the design of a monitoring module that provides the
information about the detection of a fault and the information about the
specific fault that occurred in a class of a priori-specified fault
structures. This module is made of a bank of nonlinear adaptive estimators.
One of the nonlinear adaptive estimators is the fault detection and
approximation estimator (FDAE) used for detecting and approximating faults.
An on-line approximation model, typically based on neural approximators, is
used in the FDAE. The remaining ones are fault isolation estimators (FIEs)
used only after a fault is detected for isolation purposes. Each FIE
corresponds to a particular type of fault in pre-specified class. Under
normal operating conditions (without faults), the FDAE is the only one
monitoring the system. Once a fault is detected, then the bank of FIEs is
activated and the FDAE goes into the mode of approximating the fault
function. The case that none of the isolation estimators matches the
occurred fault (to some reasonable degree) corresponds to the occurrence of
a new and unknown type of fault, and the approximated fault model can then
be used to update the fault class and also the bank of isolation estimators.
The fault model generated either by the isolation estimators (in the case of
a match) or the detection/approximation estimator is used for fault
diagnosis and provides the information to be used by the controller module
for fault accommodation. In the lecture, a complete analysis of the above
scheme will be carried out in a tutorial but rigorous way and some
simulation examples will be also reported, showing the practical aspects
involved in the use of this approach to monitor nonlinear uncertain systems
in presence of the possible occurrence of faults and malfunctions.