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Bearing fault detection by a novel condition-monitoring scheme based on statistical-time features and neural networks
dc.contributor.author | Delgado Prieto, Miquel |
dc.contributor.author | Cirrincione, Giansalvo |
dc.contributor.author | García Espinosa, Antonio |
dc.contributor.author | Ortega Redondo, Juan Antonio |
dc.contributor.author | Henao, Humberto |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria Elèctrica |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica |
dc.date.accessioned | 2013-06-19T07:54:17Z |
dc.date.created | 2013-08 |
dc.date.issued | 2013-08 |
dc.identifier.citation | Delgado, M. [et al.]. Bearing fault detection by a novel condition-monitoring scheme based on statistical-time features and neural networks. "IEEE transactions on industrial electronics", Agost 2013, vol. 30, núm. 8, p. 3398-3407. |
dc.identifier.issn | 0278-0046 |
dc.identifier.uri | http://hdl.handle.net/2117/19572 |
dc.description.abstract | Bearing degradation is the most common source of faults in electrical machines. In this context, this work presents a novel monitoring scheme applied to diagnose bearing faults. Apart from detecting local defects, i.e., single-point ball and raceway faults, it takes also into account the detection of distributed defects, such as roughness. The development of diagnosis methodologies considering both kinds of bearing faults is, nowadays, subject of concern in fault diagnosis of electrical machines. First, the method analyzes the most significant statistical-time features calculated from vibration signal. Then, it uses a variant of the curvilinear component analysis, a nonlinear manifold learning technique, for compression and visualization of the feature behavior. It allows interpreting the underlying physical phenomenon. This technique has demonstrated to be a very powerful and promising tool in the diagnosis area. Finally, a hierarchical neural network structure is used to perform the classification stage. The effectiveness of this condition-monitoring scheme has been verified by experimental results obtained from different operating conditions. |
dc.format.extent | 10 p. |
dc.language.iso | eng |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) |
dc.subject | Àrees temàtiques de la UPC::Enginyeria mecànica::Processos de fabricació mecànica::Màquines i mecanismes |
dc.subject | Àrees temàtiques de la UPC::Enginyeria mecànica::Motors::Motors elèctrics |
dc.subject | Àrees temàtiques de la UPC::Energies::Energia elèctrica::Electricitat |
dc.subject.lcsh | Ball-bearings |
dc.subject.lcsh | Electric motors, Induction |
dc.subject.lcsh | Neural networks (Computer science) |
dc.subject.lcsh | Electric fault location |
dc.subject.lcsh | Vibration--Control |
dc.subject.other | Ball bearings classification algorithms condition monitoring fault diagnosis feature extraction induction motors neural networks vibrations |
dc.title | Bearing fault detection by a novel condition-monitoring scheme based on statistical-time features and neural networks |
dc.type | Article |
dc.subject.lemac | Rodaments de boles |
dc.subject.lemac | Motors elèctrics d'inducció |
dc.subject.lemac | Xarxes neuronals (Informàtica) |
dc.subject.lemac | Energia elèctrica -- Transmissió |
dc.subject.lemac | Vibració -- Control |
dc.contributor.group | Universitat Politècnica de Catalunya. MCIA - Motion Control and Industrial Applications Research Group |
dc.identifier.doi | 10.1109/TIE.2012.2219838 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6307844 |
dc.rights.access | Restricted access - publisher's policy |
local.identifier.drac | 12494725 |
dc.description.version | Postprint (published version) |
dc.date.lift | 10000-01-01 |
local.citation.author | Delgado, M.; Cirrincione,; Garcia, A.; Ortega, J.; Henao, H. |
local.citation.publicationName | IEEE transactions on industrial electronics |
local.citation.volume | 30 |
local.citation.number | 8 |
local.citation.startingPage | 3398 |
local.citation.endingPage | 3407 |
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