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Industrial process monitoring by means of recurrent neural networks and Self Organizing Maps
dc.contributor.author | Zurita Millán, Daniel |
dc.contributor.author | Sala Cardoso, Enric |
dc.contributor.author | Cariño Corrales, Jesús Adolfo |
dc.contributor.author | Delgado Prieto, Miquel |
dc.contributor.author | Ortega Redondo, Juan Antonio |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica |
dc.date.accessioned | 2016-11-24T15:48:10Z |
dc.date.available | 2018-11-24T01:30:47Z |
dc.date.issued | 2016 |
dc.identifier.citation | Zurita, D., Sala, E., Cariño , J.A., Delgado Prieto, M., Ortega, J.A. Industrial process monitoring by means of recurrent neural networks and Self Organizing Maps. A: IEEE International Conference on Emerging Technologies and Factory Automation. "2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA): 6-9 Sept. 2016". Berlin: IEEE Press, 2016. |
dc.identifier.isbn | 978-1-5090-1314-2 |
dc.identifier.uri | http://hdl.handle.net/2117/97192 |
dc.description.abstract | Industrial manufacturing plants often suffer from reliability problems during their day-to-day operations which have the potential for causing a great impact on the effectiveness and performance of the overall process and the sub-processes involved. Time-series forecasting of critical industrial signals presents itself as a way to reduce this impact by extracting knowledge regarding the internal dynamics of the process and advice any process deviations before it affects the productive process. In this paper, a novel industrial condition monitoring approach based on the combination of Self Organizing Maps for operating point codification and Recurrent Neural Networks for critical signal modeling is proposed. The combination of both methods presents a strong synergy, the information of the operating condition given by the interpretation of the maps helps the model to improve generalization, one of the drawbacks of recurrent networks, while assuring high accuracy and precision rates. Finally, the complete methodology, in terms of performance and effectiveness is validated experimentally with real data from a copper rod industrial plant. |
dc.language.iso | eng |
dc.publisher | IEEE Press |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Automàtica i control |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Robòtica |
dc.subject.lcsh | Artificial intelligence |
dc.subject.lcsh | Machinery--Monitoring. |
dc.subject.other | condition monitoring |
dc.subject.other | knowledge acquisition |
dc.subject.other | process monitoring |
dc.subject.other | production engineering computing |
dc.subject.other | recurrent neural nets |
dc.subject.other | self-organising feature maps |
dc.subject.other | industrial process monitoring |
dc.subject.other | recurrent neural network |
dc.subject.other | self-organizing map |
dc.subject.other | industrial manufacturing plant |
dc.subject.other | reliability problem |
dc.subject.other | day-to-day operation |
dc.subject.other | critical industrial signal time-series forecasting |
dc.subject.other | knowledge extraction |
dc.subject.other | internal dynamics |
dc.subject.other | productive process |
dc.subject.other | industrial condition monitoring approach |
dc.subject.other | operating point codification |
dc.subject.other | critical signal modeling |
dc.subject.other | copper rod industrial plant |
dc.title | Industrial process monitoring by means of recurrent neural networks and Self Organizing Maps |
dc.type | Conference report |
dc.subject.lemac | Intel·ligència artificial |
dc.subject.lemac | Maquinària -- Monitoratge |
dc.contributor.group | Universitat Politècnica de Catalunya. MCIA - Motion Control and Industrial Applications Research Group |
dc.identifier.doi | 10.1109/ETFA.2016.7733534 |
dc.rights.access | Open Access |
local.identifier.drac | 19287331 |
dc.description.version | Postprint (published version) |
local.citation.author | Zurita, D.; Sala, E.; Cariño, J.A.; Delgado Prieto, M.; Ortega, J.A. |
local.citation.contributor | IEEE International Conference on Emerging Technologies and Factory Automation |
local.citation.pubplace | Berlin |
local.citation.publicationName | 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA): 6-9 Sept. 2016 |