Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163954
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dc.contributor.authorNg, Eddie Yin Kweeen_US
dc.contributor.authorLim, Jian Tiongen_US
dc.date.accessioned2022-12-27T02:21:30Z-
dc.date.available2022-12-27T02:21:30Z-
dc.date.issued2022-
dc.identifier.citationNg, E. Y. K. & Lim, J. T. (2022). Machine learning on fault diagnosis in wind turbines. Fluids, 7(12), 371-. https://dx.doi.org/10.3390/fluids7120371en_US
dc.identifier.issn2311-5521en_US
dc.identifier.urihttps://hdl.handle.net/10356/163954-
dc.description.abstractWith the improvement of wind turbine (WT) operation and maintenance (O&M) technologies and the rise of O&M cost, the fault diagnostic in WT based on supervisory control and data acquisition (SCADA) system has become one of the cheapest and easiest method to detect early alarm of fault in WT. The O&M cost was 21% of the total cost of a wind turbine project. The replacement cost of the critical parts like gearbox is twice the replacement cost for generator or blade. Hence, it is necessary to monitor the change pattern of real time parameters from the WT and maintenance action could be taken in advanced before any major failures. Therefore, a SCADA-driven fault diagnosis in WT based on machine learning algorithm has been proposed in this study by comparing the performance of three different machine learning algorithms, namely k-nearest neighbours (kNN) with bagging regressor, extreme gradient boosting (XGBoost) and artificial neural network (ANN) on condition monitoring of gearbox oil sump temperature. Beside this, this study also compared the performance of two different features selections method, namely Pearson Correlation Coefficient (PCC) and Principle Component Analysis (PCA) and three hyperparameters optimization method on optimizing the performance of the models, namely grid search, random search and Bayesian Optimization. A set of 3 years SCADA data of WT located at France have been used to verify the selected method. The result showed the kNN with bagging regressor with PCA by applying grid search provide the best R2 score and the lowest root mean square error (RMSE). The trained model can detect the potential of WT faults at least 4 weeks in advanced. However, the proposed kNN model in this study was suggested to trained with Support Vector Machine hybrid algorithm to improve its performance and reduce fault alarm. ANN could also be enhanced by applying Bayesian Physics-Informed Neural Networks as this algorithm network is more compatible to the real world non-linear dynamic system like WT.en_US
dc.language.isoenen_US
dc.relationB448en_US
dc.relation.ispartofFluidsen_US
dc.rights© 2022 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/).en_US
dc.subjectEngineering::Materialsen_US
dc.titleMachine learning on fault diagnosis in wind turbinesen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen_US
dc.identifier.doi10.3390/fluids7120371-
dc.description.versionPublished versionen_US
dc.identifier.issue12en_US
dc.identifier.volume7en_US
dc.identifier.spage371en_US
dc.subject.keywordsMachine Learningen_US
dc.subject.keywordsArtificial Neural Networken_US
dc.subject.keywordsPythonen_US
dc.subject.keywordsExtreme Gradient Boostingen_US
dc.subject.keywordsK-Nearest Neighboursen_US
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