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Evaluation of machine learning techniques for electro-mechanical system diagnosis
dc.contributor.author | Delgado Prieto, Miquel |
dc.contributor.author | García Espinosa, Antonio |
dc.contributor.author | Urresty Betancourt, Julio César |
dc.contributor.author | Riba Ruiz, Jordi-Roger |
dc.contributor.author | Ortega Redondo, Juan Antonio |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria Elèctrica |
dc.date.accessioned | 2011-10-27T13:13:30Z |
dc.date.available | 2011-10-27T13:13:30Z |
dc.date.created | 2011 |
dc.date.issued | 2011 |
dc.identifier.citation | Delgado, M. [et al.]. Evaluation of machine learning techniques for electro-mechanical system diagnosis. A: European Conference on Power Electronics and Applications. "14th European Conference on Power Electronics and Applications". Birmingham: IEEE Press. Institute of Electrical and Electronics Engineers, 2011, p. 1-10. |
dc.identifier.uri | http://hdl.handle.net/2117/13682 |
dc.description.abstract | The application of intelligent algorithms, in electro-mechanical diagnosis systems, is increasing in order to reach high Reliability and performance ratios in critical and complex scenarios. In this context, different multidimensional intelligent diagnosis systems, based on different machine learning techniques, are presented and evaluated in an electro-mechanical actuator diagnosis scheme. The used diagnosis methodology includes the acquisition of different physical magnitudes from the system, such as machine vibrations and stator currents, to enhance the monitoring capabilities. The features calculation process is based on statistical time and frequency domains features, as well as timefrequency fault indicators. A features reduction stage is, additionally, included to compress the descriptive fault information in a reduced feature set. After, different classification algorithms such as Support Vector Machines, Neural Network, k-Nearest Neighbors and Classification Trees are implemented. Classification ratios over inputs corresponding to previously learnt classes, and generalization capabilities with inputs corresponding to learnt classes slightly modified are evaluated in an experimental test bench to analyze the suitability of each algorithm for this kind of application. |
dc.format.extent | 10 p. |
dc.language.iso | eng |
dc.publisher | IEEE Press. Institute of Electrical and Electronics Engineers |
dc.subject.lcsh | Neural networks (Computer science) |
dc.subject.lcsh | Permanent magnet motors |
dc.title | Evaluation of machine learning techniques for electro-mechanical system diagnosis |
dc.type | Conference report |
dc.subject.lemac | Xarxes neuronals (Informàtica) -- Disseny |
dc.subject.lemac | Motors d'imants permanents |
dc.contributor.group | Universitat Politècnica de Catalunya. MCIA - Motion Control and Industrial Applications Research Group |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=6020305 |
dc.rights.access | Open Access |
local.identifier.drac | 6066771 |
dc.description.version | Postprint (author’s final draft) |
local.citation.author | Delgado, M.; Garcia, A.; Urresty, J.; Riba, J.; Ortega, J. |
local.citation.contributor | European Conference on Power Electronics and Applications |
local.citation.pubplace | Birmingham |
local.citation.publicationName | 14th European Conference on Power Electronics and Applications |
local.citation.startingPage | 1 |
local.citation.endingPage | 10 |