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Identification for passive robust fault detection using zonotope‐based set‐membership approaches
Author(s) -
Blesa Joaquim,
Puig Vicenç,
Saludes Jordi
Publication year - 2011
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.1242
Subject(s) - identification (biology) , fault detection and isolation , bounded function , set (abstract data type) , computer science , interval (graph theory) , fault (geology) , robustness (evolution) , algorithm , data mining , mathematics , artificial intelligence , biochemistry , chemistry , gene , mathematical analysis , botany , combinatorics , seismology , actuator , biology , programming language , geology
In this paper, the problem of identification for passive robust fault detection, when a bounded description of the modelling uncertainty is considered, is addressed. Two set‐membership identification methods are introduced to address this problem: the interval predictor and bounded error approaches. These two identification approaches naturally lead to two robust fault detection tests: the direct and inverse tests , respectively, which are also introduced and discussed. Implementation algorithms make use of a zonotope to approximate the parameter uncertainty set. Moreover, underlying hypothesis of both approaches is discussed and applicability conditions are stated. A case study based on a four‐tank system is used to illustrate the applicability and the properties of the two identification approaches as well as the corresponding fault detection. Copyright © 2011 John Wiley & Sons, Ltd.