Brunel University Research Archive (BURA) >
University >
Publications >

Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/6423

Title: On design of quantized fault detection filters with randomly occurring nonlinearities and mixed time-delays
Authors: Dong, H
Wang, Z
Gao, H
Keywords: Fault detection
Networked control systems
Randomly occurring nonlinearities
Randomly occurring mixed time-delays
Signal quantization
Publication Date: 2012
Publisher: Elsevier
Citation: Signal Processing, 92(4): 1117 - 1125, Apr 2012
Abstract: This paper is concerned with the faultdetection problem for a class of discrete-time systems with randomly occurring nonlinearities, mixed stochastic time-delays as well as measurement quantizations. The nonlinearities are assumed to occur in a random way. The mixedtime-delays comprise both the multiple discrete time-delays and the infinite distributed delays that occur in a random way as well. A sequence of stochastic variables is introduced to govern the random occurrences of the nonlinearities, discrete time-delays and distributed time-delays, where all the stochastic variables are mutually independent but obey the Bernoulli distribution. The main purpose of this paper is to design a fault detection filter such that, in the presence of measurement quantization, the overall fault detection dynamics is exponentially stable in the mean square and, at the same time, the error between the residual signal and the fault signal is made as small as possible. Sufficient conditions are first established via intensive stochastic analysis for the existence of the desired fault detection filters, and then the explicit expression of the desired filter gains is derived by means of the feasibility of certain matrix inequalities. Also, the optimal performance index for the addressed fault detection problem can be obtained by solving an auxiliary convex optimization problem. A practical example is provided to show the usefulness and effectiveness of the proposed design method.
Description: This is the post-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2012 Elsevier
Sponsorship: This work was supported in part by the National Natural Science Foundation of China under Grants 61028008, 90916005, 61004067, Fok Ying Tung Education Foundation under Grant 111064, the Foundation for the Author of National Excellent Doctoral Dissertation of China under Grant 90916005, the Engineering and Physical Sciences Research Council (EPSRC) of the U.K. under Grant GR/S27658/01, the Royal Society of the U.K. and the Alexander von Humboldt Foundation of Germany.
URI: http://www.sciencedirect.com/science/article/pii/S0165168411003872
http://bura.brunel.ac.uk/handle/2438/6423
DOI: http://dx.doi.org/10.1016/j.sigpro.2011.11.002
ISSN: 0165-1684
Appears in Collections:Information Systems and Computing
School of Information Systems, Computing and Mathematics Research Papers
Publications
Publications

Files in This Item:

File Description SizeFormat
Fulltext.pdf267.2 kBAdobe PDFView/Open

Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.

 


Library (c) Brunel University.    Powered By: DSpace
Send us your
Feedback. Last Updated: September 14, 2010.
Managed by:
Hassan Bhuiyan