Mostra el registre d'ítem simple
Robust neural-network-based fault detection with sequential D-optimum bounded-error input design
dc.contributor.author | Akielaszek-Witczak, Anna |
dc.contributor.author | Mrugalska, Beata |
dc.contributor.author | Puig Cayuela, Vicenç |
dc.contributor.author | Wyrwicka, Magdalena |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial |
dc.date.accessioned | 2016-02-11T07:55:33Z |
dc.date.issued | 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.isbn | 2405-8963 |
dc.identifier.uri | http://hdl.handle.net/2117/82806 |
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. |
dc.format.extent | 6 p. |
dc.language.iso | eng |
dc.publisher | International Federation of Automatic Control (IFAC) |
dc.subject | Àrees temàtiques de la UPC::Informàtica |
dc.subject.lcsh | Neural networks (Computer science) |
dc.subject.lcsh | Robust control |
dc.subject.other | Neural networks |
dc.subject.other | system identification |
dc.subject.other | optimum experiment design |
dc.subject.other | fault detection |
dc.subject.other | robustness |
dc.subject.other | bounded disturbances |
dc.title | Robust neural-network-based fault detection with sequential D-optimum bounded-error input design |
dc.type | Conference report |
dc.subject.lemac | Xarxes neuronals (Informàtica) |
dc.subject.lemac | Control de robustesa |
dc.contributor.group | Universitat Politècnica de Catalunya. SAC - Sistemes Avançats de Control |
dc.identifier.doi | 10.1016/j.ifacol.2015.09.565 |
dc.rights.access | Restricted access - publisher's policy |
local.identifier.drac | 17405267 |
dc.description.version | Postprint (author's final draft) |
dc.date.lift | 10000-01-01 |
local.citation.author | Akielaszek-Witczak, A.; Mrugalska, B.; Puig, V.; Wyrwicka, M. |
local.citation.contributor | IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes |
local.citation.pubplace | París |
local.citation.publicationName | 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 |
local.citation.startingPage | 434 |
local.citation.endingPage | 439 |