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AuthorEl-Koujok, M.
AuthorBenammar, M.
AuthorMeskin, N.
AuthorAl-Naemi, M.
AuthorLangari, R.
Available date2016-03-30T08:22:56Z
Publication Date2014-02
Publication NameInformation Sciences
ResourceScopus
Identifierhttp://dx.doi.org/10.1016/j.ins.2013.04.012
CitationEl-Koujok, M., Benammar, M., Meskin, N., Al-Naemi, M., Langari, R. "Multiple sensor fault diagnosis by evolving data-driven approach", (2014) Information Sciences, 259, pp. 346-358.
ISSN0020-0255
URIhttp://hdl.handle.net/10576/4278
AbstractSensors are indispensable components of modern plants and processes and their reliability is vital to ensure reliable and safe operation of complex systems. In this paper, the problem of design and development of a data-driven Multiple Sensor Fault Detection and Isolation (MSFDI) algorithm for nonlinear processes is investigated. The proposed scheme is based on an evolving multi-Takagi Sugeno framework in which each sensor output is estimated using a model derived from the available input/output measurement data. Our proposed MSFDI algorithm is applied to Continuous-Flow Stirred-Tank Reactor (CFSTR). Simulation results demonstrate and validate the performance capabilities of our proposed MSFDI algorithm.
SponsorQatar Foundation (Project: NPRP 09-393-2-145/1/2011).
Languageen
PublisherElsevier Inc.
SubjectData-driven approach
Nonlinear system
Sensor fault diagnosis
TitleMultiple sensor fault diagnosis by evolving data-driven approach
TypeArticle
Pagination346-358
Volume Number259
dc.accessType Abstract Only


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