Dirt and mud detection and diagnosis on a wind turbine blade employing guided waves and supervised learning classifiers

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dc.contributor.author Arcos Jiménez, Alfredo
dc.contributor.author Gómez Muñoz, Carlos Quiterio
dc.contributor.author García Márquez, Fausto Pedro
dc.date.accessioned 2018-03-01T12:46:26Z
dc.date.available 2018-03-01T12:46:26Z
dc.date.issued 2019
dc.identifier.citation Jiménez, A. A., Muñoz, C. Q. G., & Márquez, F. P. G. (2019). Dirt and Mud Detection and Diagnosis on a Wind Turbine Blade employing Guided Waves and Supervised Learning Classifiers. Reliability Engineering & System Safety, 184, 2-12. https://doi.org/10.1016/j.ress.2018.02.013 spa
dc.identifier.issn 0951-8320
dc.identifier.uri http://hdl.handle.net/11268/7096
dc.description.abstract Dirt and mud on wind turbine blades (WTB) reduce productivity and can generate stops and downtimes. A condition monitoring system based on non-destructive tests by ultrasonic waves was used to analyse it. This paper employs an approach that considers advanced signal processing and machine learning to determine the thickness of the dirt and mud in a WTB. Firstly, the signal is filtered by Wavelet transform. FE and Feature Selection(FS) are employed to remove non-useful data and redundant features. FS selects the number of the most significant terms of the model for fault detection and identification, reducing the dimension of the dataset. Pattern recognition is carried out by the following supervised learning classifiers based on statistical models to calculate and classify the signal depending on the fault: Ensemble Subspace Discriminant; k-Nearest Neighbours; Linear Support Vector Machine; Linear Discriminant Analysis; Decision Trees. Receiver Operating Characteristic analysis is used to evaluate the classifiers. Neighbourhood Component Analysis has been employed in feature selection. Several case studies of mud on the WTB surface have been considered to test and validate the approach. Autoregressive (AR) model and Principal Component Analysis (PCA) have been employed to FE. The results provided by PCA show an improvement on the AR results. The novelty of this work is focused on applying this approach to detect and diagnose mud and dirt in WTB. spa
dc.description.sponsorship Ministerio de Economía y Competitividad DPI2015-67264-P spa
dc.description.sponsorship Ministerio de Economía y Competitividad RTC-2016-5694-3 spa
dc.language.iso eng spa
dc.subject.other Macro fiber composite spa
dc.subject.other Wavelet transforms spa
dc.title Dirt and mud detection and diagnosis on a wind turbine blade employing guided waves and supervised learning classifiers spa
dc.type article spa
dc.description.impact 4.139 JCR (2017) Q1, 5/47 Engineering, Industrial, 6/83 Operations Research and Management Science spa
dc.identifier.doi 10.1016/j.ress.2018.02.013
dc.rights.accessRights closedAccess spa
dc.subject.uem Turbinas spa
dc.subject.uem Ingeniería spa
dc.subject.unesco Turbina spa
dc.subject.unesco Mantenimiento spa
dc.description.filiation UEM spa
dc.peerreviewed Si spa

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