Sensors Data Analysis in Supervisory Control and Data Acquisition (SCADA) Systems to Foresee Failures with an Undetermined Origin
dc.contributor.author | Maseda Rego, Francisco Javier | |
dc.contributor.author | López, Iker | |
dc.contributor.author | Martija López, Itziar ![]() | |
dc.contributor.author | Alkorta Egiguren, Patxi | |
dc.contributor.author | Garrido Hernández, Aitor Josu ![]() | |
dc.contributor.author | Garrido Hernández, Izaskun ![]() | |
dc.date.accessioned | 2021-04-30T10:35:22Z | |
dc.date.available | 2021-04-30T10:35:22Z | |
dc.date.issued | 2021-04-14 | |
dc.identifier.citation | Sensors 21(8) : (2021) // Article ID 2762 | es_ES |
dc.identifier.issn | 1424-8220 | |
dc.identifier.uri | http://hdl.handle.net/10810/51267 | |
dc.description.abstract | This paper presents the design and implementation of a supervisory control and data acquisition (SCADA) system for automatic fault detection. The proposed system offers advantages in three areas: the prognostic capacity for preventive and predictive maintenance, improvement in the quality of the machined product and a reduction in breakdown times. The complementary technologies, the Industrial Internet of Things (IIoT) and various machine learning (ML) techniques, are employed with SCADA systems to obtain the objectives. The analysis of different data sources and the replacement of specific digital sensors with analog sensors improve the prognostic capacity for the detection of faults with an undetermined origin. Also presented is an anomaly detection algorithm to foresee failures and to recognize their occurrence even when they do not register as alarms or events. The improvement in machine availability after the implementation of the novel system guarantees the accomplishment of the proposed objectives. | es_ES |
dc.description.sponsorship | This work was supported partially by the Basque Government through project IT1207-19, and by the MCIU/MINECO through RTI2018-094902-B-C21/RTI2018-094902-B-C22 (MCIU/AEI/FEDER, UE). The authors would like to thank Intenance Company for its collaboration and help. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.relation | info:eu-repo/grantAgreement/MCIU/RTI2018-094902-B-C21 | es_ES |
dc.relation | info:eu-repo/grantAgreement/MCIU/RTI2018-094902-B-C22 | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | |
dc.subject | industry 4.0 | es_ES |
dc.subject | industrial internet of things | es_ES |
dc.subject | supervisory control and data acquisition system | es_ES |
dc.subject | machine learning | es_ES |
dc.title | Sensors Data Analysis in Supervisory Control and Data Acquisition (SCADA) Systems to Foresee Failures with an Undetermined Origin | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.date.updated | 2021-04-23T13:33:46Z | |
dc.rights.holder | 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | es_ES |
dc.relation.publisherversion | https://www.mdpi.com/1424-8220/21/8/2762/htm | es_ES |
dc.identifier.doi | 10.3390/s21082762 | |
dc.departamentoes | Ingeniería de sistemas y automática | |
dc.departamentoeu | Sistemen ingeniaritza eta automatika |
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Except where otherwise noted, this item's license is described as 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).