dc.creator | Cabrera, Diego | es |
dc.creator | Sancho Caparrini, Fernando | es |
dc.creator | Tobar, Felipe | es |
dc.date.accessioned | 2021-04-15T10:12:56Z | |
dc.date.available | 2021-04-15T10:12:56Z | |
dc.date.issued | 2017 | |
dc.identifier.citation | Cabrera, D., Sancho Caparrini, F. y Tobar, F. (2017). Combining reservoir computing and variational inference for efficient one-class learning on dynamical systems. En SDPC 2017: International Conference on Sensing, Diagnostics, Prognostics, and Control (57-62), Shanghai, China: IEEE Computer Society. | |
dc.identifier.isbn | 978-1-5090-4020-9 | es |
dc.identifier.uri | https://hdl.handle.net/11441/107120 | |
dc.description.abstract | Usually, time series acquired from some measurement
in a dynamical system are the main source of information
about its internal structure and complex behavior. In
this situation, trying to predict a future state or to classify
internal features in the system becomes a challenging task that
requires adequate conceptual and computational tools as well as
appropriate datasets. A specially difficult case can be found in
the problems framed under one-class learning. In an attempt to
sidestep this issue, we present a machine learning methodology
based in Reservoir Computing and Variational Inference. In
our setting, the dynamical system generating the time series is
modeled by an Echo State Network (ESN), and the parameters
of the ESN are defined by an expressive probability distribution
which is represented as a Variational Autoencoder. As a proof of
its applicability, we show some results obtained in the context
of condition-based maintenance in rotating machinery, where
vibration signals can be measured from the system, our goal
is fault detection in helical gearboxes under realistic operating
conditions. The results show that our model is able, after trained
only with healthy conditions, to discriminate successfully between
healthy and faulty conditions. | es |
dc.description.sponsorship | Ministerio de Economía y Competitividad TIN2012-37434 | es |
dc.description.sponsorship | Ministerio de Economía y Competitividad TIN2013-41086-P | es |
dc.format | application/pdf | es |
dc.format.extent | 6 | es |
dc.language.iso | eng | es |
dc.publisher | IEEE Computer Society | es |
dc.relation.ispartof | SDPC 2017: International Conference on Sensing, Diagnostics, Prognostics, and Control (2017), pp. 57-62. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Dynamical System Modeling | es |
dc.subject | Reservoir Computing | es |
dc.subject | Variational Inference | es |
dc.title | Combining reservoir computing and variational inference for efficient one-class learning on dynamical systems | es |
dc.type | info:eu-repo/semantics/conferenceObject | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial | es |
dc.relation.projectID | TIN2012-37434 | es |
dc.relation.projectID | TIN2013-41086-P | es |
dc.relation.publisherversion | https://ieeexplore.ieee.org/document/8181550 | es |
dc.identifier.doi | 10.1109/SDPC.2017.21 | es |
dc.publication.initialPage | 57 | es |
dc.publication.endPage | 62 | es |
dc.eventtitle | SDPC 2017: International Conference on Sensing, Diagnostics, Prognostics, and Control | es |
dc.eventinstitution | Shanghai, China | es |
dc.relation.publicationplace | New York, USA | es |
dc.contributor.funder | Ministerio de Economía y Competitividad (MINECO). España | es |