Robust neural-network-based fault detection with sequential D-optimum bounded-error input design

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dc.contributor	Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.contributor	Universitat Politècnica de Catalunya. SAC - Sistemes Avançats de Control
dc.contributor.author	Akielaszek-Witczak, Anna
dc.contributor.author	Mrugalska, Beata
dc.contributor.author	Puig Cayuela, Vicenç
dc.contributor.author	Wyrwicka, Magdalena
dc.date	2015
dc.identifier.citation	Akielaszek-Witczak, A., Mrugalska, B., Puig, V., Wyrwicka, M. Robust neural-network-based fault detection with sequential D-optimum bounded-error input design. A: IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes. "IFAC-PapersOnLine (volume 48, issue 21, Pages 1-1496): 9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes SAFEPROCESS 2015 Paris, 2–4 September 2015". París: International Federation of Automatic Control (IFAC), 2015, p. 434-439.
dc.identifier.citation	2405-8963
dc.identifier.citation	10.1016/j.ifacol.2015.09.565
dc.identifier.uri	http://hdl.handle.net/2117/82806
dc.language.iso	eng
dc.publisher	International Federation of Automatic Control (IFAC)
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	Àrees temàtiques de la UPC::Informàtica
dc.subject	Neural networks (Computer science)
dc.subject	Robust control
dc.subject	Neural networks
dc.subject	system identification
dc.subject	optimum experiment design
dc.subject	fault detection
dc.subject	robustness
dc.subject	bounded disturbances
dc.subject	Xarxes neuronals (Informàtica)
dc.subject	Control de robustesa
dc.title	Robust neural-network-based fault detection with sequential D-optimum bounded-error input design
dc.type	info:eu-repo/semantics/submittedVersion
dc.type	info:eu-repo/semantics/conferenceObject
dc.description.abstract	A growing demand for technologically advanced systems has contributed to the increase of the awareness of systems safety and reliability. Such a situation requires the development of novel methods of robust fault diagnosis. The application of the analytical redundancy based methods for system fault detection causes that theIr effectiveness depends on model quality. In this paper, a new Methodology for the improvement of the neural model with a D-optimum sequential experimental design technique combined with outer bounding ellipsoid algorithm is proposed. Moreover, a novel method of robust fault detection against neural model uncertainty and disturbances is developed. Such an approach is used for modelling and robust fault detection of the three-screw spindle oil pump.

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