000069643 001__ 69643
000069643 005__ 20200108100348.0
000069643 0247_ $$2doi$$a10.1016/j.ress.2017.10.004
000069643 0248_ $$2sideral$$a101763
000069643 037__ $$aART-2018-101763
000069643 041__ $$aeng
000069643 100__ $$0(orcid)0000-0002-3830-9308$$aReder, Maik
000069643 245__ $$aData-driven learning framework for associating weather conditions and wind turbine failures
000069643 260__ $$c2018
000069643 5060_ $$aAccess copy available to the general public$$fUnrestricted
000069643 5203_ $$aThe need for cost effective operation and maintenance (O&M) strategies in wind farms has risen significantly with the growing wind energy sector. In order to decrease costs, current practice in wind farm O&M is switching from corrective and preventive strategies to rather predictive ones. Anticipating wind turbine (WT) failures requires sophisticated models to understand the complex WT component degradation processes and to facilitate maintenance decision making. Environmental conditions and their impact on WT reliability play a significant role in these processes and need to be investigated profoundly. This paper is presenting a framework to assess and correlate weather conditions and their effects on WT component failures. Two approaches, using (a) supervised and (b) unsupervised data mining techniques are applied to pre-process the weather and failure data. An apriori rule mining algorithm is employed subsequently, in order to obtain logical interconnections between the failure occurrences and the environmental data, for both approaches. The framework is tested using a large historical failure database of modern wind turbines. The results show the relation between environmental parameters such as relative humidity, ambient temperature, wind speed and the failures of five major WT components: gearbox, generator, frequency converter, pitch and yaw system. Additionally, the performance of each technique, associating weather conditions and WT component failures, is assessed.
000069643 536__ $$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 642108-AWESOME$$9info:eu-repo/grantAgreement/EC/H2020/642108/EU/Advanced Wind Energy Systems Operation and Maintenance Expertise/AWESOME
000069643 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000069643 590__ $$a4.039$$b2018
000069643 591__ $$aOPERATIONS RESEARCH & MANAGEMENT SCIENCE$$b11 / 84 = 0.131$$c2018$$dQ1$$eT1
000069643 591__ $$aENGINEERING, INDUSTRIAL$$b5 / 46 = 0.109$$c2018$$dQ1$$eT1
000069643 592__ $$a1.944$$b2018
000069643 593__ $$aApplied Mathematics$$c2018$$dQ1
000069643 593__ $$aSafety, Risk, Reliability and Quality$$c2018$$dQ1
000069643 593__ $$aIndustrial and Manufacturing Engineering$$c2018$$dQ1
000069643 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000069643 700__ $$aYürüsen, Nurseda Y.
000069643 700__ $$0(orcid)0000-0003-2360-0845$$aMelero, Julio J.$$uUniversidad de Zaragoza
000069643 7102_ $$15009$$2535$$aUniversidad de Zaragoza$$bDpto. Ingeniería Eléctrica$$cÁrea Ingeniería Eléctrica
000069643 773__ $$g169 (2018), 554-559$$pReliab. eng. syst. saf.$$tRELIABILITY ENGINEERING & SYSTEM SAFETY$$x0951-8320
000069643 8564_ $$s3393976$$uhttps://zaguan.unizar.es/record/69643/files/texto_completo.pdf$$yPreprint
000069643 8564_ $$s74625$$uhttps://zaguan.unizar.es/record/69643/files/texto_completo.jpg?subformat=icon$$xicon$$yPreprint
000069643 909CO $$ooai:zaguan.unizar.es:69643$$particulos$$pdriver
000069643 951__ $$a2020-01-08-09:31:28
000069643 980__ $$aARTICLE