By Topic

Kalman filter for parametric fault detection: an internal model principle-based 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
$31 $31
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)
Doraiswami, R. ; Dept. of Electr. & Comput. Eng., Univ. of New Brunswick, Fredericton, NB, Canada ; Cheded, L.

The paramount importance of fault detection (FD) in complex engineering systems has undoubtedly been the main driver behind the development of a plethora of techniques in the FD area. In this study, the authors propose an internal model principle-based Kalman filter (IMP-KF) structure for use in the detection of parametric faults. The authors show that the closed-loop structure of the IMP-KF is indeed a necessary and sufficient condition for generating residuals upon which the FD process hinges. They advocate a residual generator structure similar to that used in the standard Kalman filtering (KF), and judiciously exploit the non-robustness to model mismatch of the proposed IMP-KF scheme to detect faults in the presence of noise and disturbances. With no model mismatch, the KF residual's whiteness is exploited to derive a composite hypothesis testing that accounts for a low probability for false alarm and a high probability of correct decision for various reference inputs. The proposed scheme was successfully evaluated on both simulated and physical systems.

Published in:

Control Theory & Applications, IET  (Volume:6 ,  Issue: 5 )