Advanced Search

Query:

Fill in the form and click »Search«...

Format:

Display: entries per page entries

Zbl 0972.93007
Polycarpou, Marios M.; Trunov, Alexander B.
Learning approach to nonlinear fault diagnosis: Detectability analysis. (English)
[J] IEEE Trans. Autom. Control 45, No.4, 806-812 (2000). ISSN 0018-9286

Summary: The learning approach to fault diagnosis provides a methodology for designing monitoring architectures which can be used for detection, identification and accommodation of failures in dynamical systems. This paper considers the issues of detectability conditions and detection time in a nonlinear fault diagnosis scheme based on the learning approach. First, conditions are derived to characterize the range of detectable faults. Then, nonconservative upper bounds are computed for the detection time of incipient and abrupt faults. It is shown that the detection time bound decreases monotonically as the values of certain design parameters increase. The theoretical results are illustrated by a simulation example of a second-order system.

MSC 2000:: *93B07 Observability
90B25 Reliability, etc.
94A13 Detection theory
68U05 Computational geometry, etc.

Keywords: learning approach; fault diagnosis; detectability; detection time

Comment on this Item PDF MathML XML ASCII DVI PS BibTeX Online Ordering Article Journal

	Scientific prize winners of the ICM 2010
	Overhang
	Lie groups, physics and geometry. An introduction for physicists, engineers and chemists.

Advanced Search

Zentralblatt MATH Berlin [Germany]