dc.contributor.author | Jankauskas, Mindaugas | |
dc.contributor.author | Serackis, Artūras | |
dc.contributor.author | Šapurov, Martynas | |
dc.contributor.author | Pomarnacki, Raimondas | |
dc.contributor.author | Baškys, Algirdas | |
dc.contributor.author | Hyunh, Van Khang | |
dc.contributor.author | Vaimann, Toomas | |
dc.contributor.author | Zakis, Janis | |
dc.date.accessioned | 2023-09-18T16:34:49Z | |
dc.date.available | 2023-09-18T16:34:49Z | |
dc.date.issued | 2023 | |
dc.identifier.issn | 1424-8220 | |
dc.identifier.uri | https://etalpykla.vilniustech.lt/handle/123456789/115166 | |
dc.description.abstract | The aim of the presented investigation is to explore the time gap between an anomaly appearance in continuously measured parameters of the device and a failure, related to the end of the remaining resource of the device-critical component. In this investigation, we propose a recurrent neural network to model the time series of the parameters of the healthy device to detect anomalies by comparing the predicted values with the ones actually measured. An experimental investigation was performed on SCADA estimates received from different wind turbines with failures. A recurrent neural network was used to predict the temperature of the gearbox. The comparison of the predicted temperature values and the actual measured ones showed that anomalies in the gearbox temperature could be detected up to 37 days before the failure of the device-critical component. The performed investigation compared different models that can be used for temperature time-series modeling and the influence of selected input features on the performance of temperature anomaly detection. | eng |
dc.format | PDF | |
dc.format.extent | p. 1-10 | |
dc.format.medium | tekstas / txt | |
dc.language.iso | eng | |
dc.relation.isreferencedby | Science Citation Index Expanded (Web of Science) | |
dc.relation.isreferencedby | Scopus | |
dc.relation.isreferencedby | DOAJ | |
dc.relation.isreferencedby | CABI (abstracts) | |
dc.relation.isreferencedby | INSPEC | |
dc.rights | Laisvai prieinamas internete | |
dc.source.uri | https://www.mdpi.com/1424-8220/23/12/5695 | |
dc.source.uri | https://talpykla.elaba.lt/elaba-fedora/objects/elaba:169961761/datastreams/MAIN/content | |
dc.title | Exploring the limits of early predictive maintenance in wind turbines applying an anomaly detection technique | |
dc.type | Straipsnis Web of Science DB / Article in Web of Science DB | |
dcterms.accessRights | 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/) | |
dcterms.license | Creative Commons – Attribution – ShareAlike – 4.0 International | |
dcterms.references | 32 | |
dc.type.pubtype | S1 - Straipsnis Web of Science DB / Web of Science DB article | |
dc.contributor.institution | Vilniaus Gedimino technikos universitetas | |
dc.contributor.institution | Vilniaus Gedimino technikos universitetas Valstybinis mokslinių tyrimų institutas Fizinių ir technologijos mokslų centras | |
dc.contributor.institution | University of Agder, Kristiansand | |
dc.contributor.institution | Tallinn University of Technology | |
dc.contributor.institution | Riga Technical University | |
dc.contributor.faculty | Elektronikos fakultetas / Faculty of Electronics | |
dc.subject.researchfield | T 001 - Elektros ir elektronikos inžinerija / Electrical and electronic engineering | |
dc.subject.researchfield | N 002 - Fizika / Physics | |
dc.subject.vgtuprioritizedfields | MC0505 - Inovatyvios elektroninės sistemos / Innovative Electronic Systems | |
dc.subject.ltspecializations | L104 - Nauji gamybos procesai, medžiagos ir technologijos / New production processes, materials and technologies | |
dc.subject.en | SCADA | |
dc.subject.en | wind turbine | |
dc.subject.en | anomaly | |
dc.subject.en | temperature | |
dc.subject.en | neural network | |
dcterms.sourcetitle | Sensors | |
dc.description.issue | iss. 12 | |
dc.description.volume | vol. 23 | |
dc.publisher.name | MDPI AG | |
dc.publisher.city | Basel | |
dc.identifier.doi | 001017782500001 | |
dc.identifier.doi | 10.3390/s23125695 | |
dc.identifier.elaba | 169961761 | |