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Enhanced Industrial Machinery Condition Monitoring Methodology based on Novelty Detection and Multi-Modal Analysis
dc.contributor.author | Cariño Corrales, Jesús Adolfo |
dc.contributor.author | Delgado Prieto, Miquel |
dc.contributor.author | Zurita Millán, Daniel |
dc.contributor.author | Millan, Marta |
dc.contributor.author | Ortega Redondo, Juan Antonio |
dc.contributor.author | Romero Troncoso, Rene De Jesus |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica |
dc.date.accessioned | 2017-03-02T16:02:27Z |
dc.date.available | 2017-03-02T16:02:27Z |
dc.date.issued | 2016-10-19 |
dc.identifier.citation | Cariño , J.A., Delgado Prieto, M., Zurita, D., Millan, M., Ortega, J.A., Romero-Troncoso, R. Enhanced Industrial Machinery Condition Monitoring Methodology based on Novelty Detection and Multi-Modal Analysis. "IEEE access", 19 Octubre 2016, vol. 4, p. 7594-7604. |
dc.identifier.issn | 2169-3536 |
dc.identifier.uri | http://hdl.handle.net/2117/101874 |
dc.description.abstract | This paper presents a condition-based monitoring methodology based on novelty detection applied to industrial machinery. The proposed approach includes both, the classical classification of multiple a priori known scenarios, and the innovative detection capability of new operating modes not previously available. The development of condition-based monitoring methodologies considering the isolation capabilities of unexpected scenarios represents, nowadays, a trending topic able to answer the demanding requirements of the future industrial processes monitoring systems. First, the method is based on the temporal segmentation of the available physical magnitudes, and the estimation of a set of time-based statistical features. Then, a double feature reduction stage based on Principal Component Analysis and Linear Discriminant Analysis is applied in order to optimize the classification and novelty detection performances. The posterior combination of a Feed-forward Neural Network and One-Class Support Vector Machine allows the proper interpretation of known and unknown operating conditions. The effectiveness of this novel condition monitoring scheme has been verified by experimental results obtained from an automotive industry machine. |
dc.format.extent | 11 p. |
dc.language.iso | eng |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) |
dc.subject | Àrees temàtiques de la UPC::Enginyeria electrònica |
dc.subject | Àrees temàtiques de la UPC::Enginyeria mecànica::Processos de fabricació mecànica |
dc.subject.lcsh | Machine learning |
dc.subject.other | Condition Monitoring |
dc.subject.other | Fault Detection |
dc.subject.other | Machine Learning |
dc.subject.other | Novelty Detection. |
dc.title | Enhanced Industrial Machinery Condition Monitoring Methodology based on Novelty Detection and Multi-Modal Analysis |
dc.type | Article |
dc.subject.lemac | Maquinària |
dc.subject.lemac | Aprenentatge automàtic |
dc.subject.lemac | Màquines, Teoria de |
dc.contributor.group | Universitat Politècnica de Catalunya. MCIA - Motion Control and Industrial Applications Research Group |
dc.identifier.doi | 10.1109/ACCESS.2016.2619382 |
dc.relation.publisherversion | http://ieeexplore.ieee.org/document/7600383/ |
dc.rights.access | Open Access |
local.identifier.drac | 19727632 |
dc.description.version | Postprint (published version) |
local.citation.author | Cariño, J.A.; Delgado Prieto, M.; Zurita, D.; Millan, M.; Ortega, J.A.; Romero-Troncoso, R.J. |
local.citation.publicationName | IEEE access |
local.citation.volume | 4 |
local.citation.startingPage | 7594 |
local.citation.endingPage | 7604 |
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