By Topic

Automated fault detection and accommodation: a learning systems approach

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
M. M. Polycarpou ; Dept. of Electr. & Comput. Eng., Cincinnati Univ., OH, USA ; A. J. Helmicki

The detection, diagnosis, and accommodation of system failures or degradations are becoming increasingly more important in modern engineering problems. A system failure often causes changes in critical system parameters, or even, changes in the nonlinear dynamics of the system. This paper presents a general framework for constructing automated fault diagnosis and accommodation architectures using on-line approximators and adaptation/learning schemes. In this framework, neural network models constitute an important class of on-line approximators. Changes in the system dynamics are monitored by an on-line approximation model, which is used not only for detecting but also for accommodating failures. A systematic procedure for constructing nonlinear estimation algorithms is developed, and a stable learning scheme is derived using Lyapunov theory. Simulation studies are used to illustrate the results and to gain intuition into the selection of design parameters

Published in:

IEEE Transactions on Systems, Man, and Cybernetics  (Volume:25 ,  Issue: 11 )