Diagnoses of additive faults of serial wounded motor using artificial intelligence methods

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dc.contributor.authorFüvesi, Viktor
dc.contributor.authorDr.Kovács, Ernő
dc.date.accessioned2015-02-20T10:25:02Z
dc.date.available2015-02-20T10:25:02Z
dc.date.issued2014
dc.description.abstractIn this paper an artificial neural network based technique will be introduce, which is capable to separate the different types of faulty state of the analysed system and generate signs to alarm the user about the failures in the system. The method can detect, separate, and identify the faults in the system. Large datasets were developed to train the separator networks. To speed up the training process of separator network, active learning method was applied. To find the separator mathematical structure weakness, a complex test process was used where the size of the different faults was varied and the performance of the structure was examined. The examination has two parts: first the appearance and termination of the faults were tested; later the estimation of the fault size was checked. The separator technique needs mathematical models of the analysed system. In our case, the models were also based on feed forward neural networks.hu_HU
dc.identifier.doi10.17667/riim.2014.1-2/6.hu_HU
dc.identifier.issn2064-9622hu_HU
dc.identifier.issueNo. 1-2.hu_HU
dc.identifier.urihttp://hdl.handle.net/2437/205772
dc.identifier.volumeVol. 1. (2014).hu_HU
dc.language.isoenhu_HU
dc.rightsNevezd meg! - Ne add el! - Ne változtasd! 2.5 Magyarország*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/hu/*
dc.subjectartificial intelligencehu_HU
dc.subjectfault diagnostichu_HU
dc.subjectactuatorhu_HU
dc.titleDiagnoses of additive faults of serial wounded motor using artificial intelligence methodshu_HU
dc.typeArticlehu_HU
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Recent Innovations in Mechatronics