Zobrazeno 1 - 10
of 11
pro vyhledávání: '"Alberto Diez-Olivan"'
Autor:
Patxi Ortego, David Camacho, Itziar Landa-Torres, Diego Galar, Alberto Diez-Olivan, Basilio Sierra, Javier Del Ser
Publikováno v:
IEEE Transactions on Industrial Informatics. 17:7760-7770
Industrial prognosis refers to the prediction of failures of an industrial asset based on data collected by Internet of Things sensors. Prognostic models can experience the undesired effects of concept drift, namely, the presence of nonstationary phe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7d6e1926d72ffeb319af18e94499fb75
https://doi.org/10.1109/tii.2021.3058350
https://doi.org/10.1109/tii.2021.3058350
Publikováno v:
Information Fusion. 50:92-111
The so-called “smartization” of manufacturing industries has been conceived as the fourth industrial revolution or Industry 4.0, a paradigm shift propelled by the upsurge and progressive maturity o ...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ba851b1a24915256e8a65ef95fccaba7
https://doi.org/10.1016/j.inffus.2018.10.005
https://doi.org/10.1016/j.inffus.2018.10.005
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030623647
IDEAL (2)
IDEAL (2)
The Industry 4.0 revolution allows monitoring and intelligent processing of big amounts of data. When monitoring certain assets, very few data is found for operation under faulty conditions because the cost of not operating properly is unacceptable a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a2440a214836da8d3b844c3e8eef667c
http://hdl.handle.net/11556/1035
http://hdl.handle.net/11556/1035
Publikováno v:
Soft Computing. 23:9937-9953
In many complex industrial scenarios where condition monitoring data are involved, data-driven models can highly support maintenance tasks and improve assets’ performance. To infer physical meaningful models that accurately characterize assets’ b
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::61af963113f9e92037f6a3644cbefc90
https://doi.org/10.1007/s00500-018-3549-3
https://doi.org/10.1007/s00500-018-3549-3
Publikováno v:
The International Journal of Advanced Manufacturing Technology. 95:327-340
This paper presents a novel algorithm to assess the health status in monitoring sensor data using a kernel-based support vector machine (SVM) approach. Today, accurate fault prediction is a key issue raised by maintenance. In particular, automaticall
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ba65e854251b469d61781dcc3c2c89c8
https://doi.org/10.1007/s00170-017-1204-2
https://doi.org/10.1007/s00170-017-1204-2
Autor:
Fernando Veiga, Norberto López de Lacalle, Alberto Diez-Olivan, Mari Luz Penalva, Javier Camacho, Lutz Deitert
Publikováno v:
TECNALIA Publications
Fundación Tecnalia Research & Innovation
Addi. Archivo Digital para la Docencia y la Investigación
instname
Materials
Materials, Vol 12, Iss 7, p 1157 (2019)
Volume 12
Issue 7
Fundación Tecnalia Research & Innovation
Addi. Archivo Digital para la Docencia y la Investigación
instname
Materials
Materials, Vol 12, Iss 7, p 1157 (2019)
Volume 12
Issue 7
Blind fasteners are of special interest for aircraft construction since they allow working on joints where only one side is accessible, as is the case in many common aerospace box-type structures, such as stabilizers and flaps. This paper aims to del
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e752971f370bf9ed3a04b7cd2a4faa1d
http://hdl.handle.net/11556/719
http://hdl.handle.net/11556/719
Autor:
Fernando Veiga, Basilio Sierra, Patxi Ortego, Alberto Diez-Olivan, Javier Del Ser, Mariluz Penalva
Publikováno v:
Swarm and Evolutionary Computation. 54:100650
The Industry 4.0 revolution allows gathering big amounts of data that are used to train and deploy Artificial Intelligence algorithms to solve complex industrial problems, optimally and automatically. From those, Long-Short Term Memory Fully Convolut
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::676e35cb1b7d6d0549996ecdb83b9b32
https://doi.org/10.1016/j.swevo.2020.100650
https://doi.org/10.1016/j.swevo.2020.100650
An efficient and sustainable animal production requires fine-tuning and control of all the parameters involved. But this is not a simple task. Animal farming is a complex biological system in which environmental parameters and management practices in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2daf7e09bc9bade756ec95bfb94d9e75
http://hdl.handle.net/11556/540
http://hdl.handle.net/11556/540
Publikováno v:
Neurocomputing, ISSN 0925-2312, 2017-06-02, No. 241
Archivo Digital UPM
Universidad Politécnica de Madrid
Archivo Digital UPM
Universidad Politécnica de Madrid
A data-driven prognostics approach for monitoring sensor data is proposed.It relies on a combination of constrained K-means clustering, fuzzy modeling and LOF-based score.Fully comprehensive yet accurate models are obtained and deployed in a CBM+ pla
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::26dfe5a86f9f71665dcddaf5c4174864
https://oa.upm.es/46796/
https://oa.upm.es/46796/
Autor:
Maialen Murua, Fernando Veiga Suarez, Juan Antonio Ortega Lalmolda, Alberto Diez Olivan, Mariluz Penalva Oscoz
Publikováno v:
Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
instname
TECNALIA Publications
Fundación Tecnalia Research & Innovation
DYNA
instname
TECNALIA Publications
Fundación Tecnalia Research & Innovation
DYNA
Quality control in manufacturing is a recurrent topic as the ultimate goals are to produce high quality products with less cost. Mostly, the problems related to manufacturing processes are addressed focusing on the process itself putting aside other
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d11a3a8a009317893c1b5f463630da1a