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Reliability analysis of detecting false alarms that employ neural networks: a real case study on wind turbines.

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dc.contributor.author Pliego Marugán, Alberto
dc.contributor.author Peco Chacón, Ana María
dc.contributor.author García Márquez, Fausto Pedro
dc.date.accessioned 2019-09-02T08:19:45Z
dc.date.available 2019-09-02T08:19:45Z
dc.date.issued 2019-11
dc.identifier.citation Reliability Engineering & System Safety es_ES
dc.identifier.issn 0951-8320
dc.identifier.uri http://hdl.handle.net/10578/21480
dc.description.abstract Operations and maintenance tasks are critical to the reliability of a wind turbine. The state-of-the-art demonstrates the effectiveness of reliability centred maintenance, but there are no research studies that consider false alarms to reliability of the wind turbines. This paper presents a novel approach based on artificial neural networks to reliability centred maintenance. The methodology is employed for false alarm detection and prioritization, training the artificial neural networks over the time to increase the system reliability. The approach is applied to a real dataset from a supervisory control and data acquisition system together with a vibration monitoring system of a wind turbine. The results accuracy is done by confusion matrices, studding real alarms with the estimations provided by the approach, and the results are validated with real false alarms and compared by the results given by a fuzzy logic model. The method provides accuracy results (over 90%). A novelty is to use a two real dataset from a wind turbine to create a redundant response to detect false alarms by artificial neural networks. es_ES
dc.format application/pdf es_ES
dc.language.iso en es_ES
dc.publisher Elsevier es_ES
dc.rights info:eu-repo/semantics/openAccess es_ES
dc.subject Reliability centred maintenance es_ES
dc.subject Condition monitoring es_ES
dc.subject Artificial neural network es_ES
dc.subject Wind energy conversion systems es_ES
dc.subject False alarms es_ES
dc.title Reliability analysis of detecting false alarms that employ neural networks: a real case study on wind turbines. es_ES
dc.type info:eu-repo/semantics/article es_ES
dc.relation.projectID Optimus and DPI2015-67264-P es_ES
dc.identifier.DOI https://doi.org/10.1016/j.ress.2019.106574

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