{"data":{"id":"10.6084/m9.figshare.24080729.v1","type":"dois","attributes":{"doi":"10.6084/m9.figshare.24080729.v1","prefix":"10.6084","suffix":"m9.figshare.24080729.v1","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"M., Arjun Jaikrishna","givenName":"Arjun Jaikrishna","familyName":"M.","affiliation":[],"nameIdentifiers":[]},{"name":"S, Naveen Venkatesh","givenName":"Naveen Venkatesh","familyName":"S","affiliation":[],"nameIdentifiers":[]},{"name":"V, Sugumaran","givenName":"Sugumaran","familyName":"V","affiliation":[],"nameIdentifiers":[]},{"name":"Dhanraj, Joshuva Arockia","givenName":"Joshuva Arockia","familyName":"Dhanraj","affiliation":[],"nameIdentifiers":[]},{"name":"Velmurugan, Karthikeyan","givenName":"Karthikeyan","familyName":"Velmurugan","affiliation":[],"nameIdentifiers":[]},{"name":"Sirisamphanwong, Chatchai","givenName":"Chatchai","familyName":"Sirisamphanwong","affiliation":[],"nameIdentifiers":[]},{"name":"Ngoenmeesri, Rattaporn","givenName":"Rattaporn","familyName":"Ngoenmeesri","affiliation":[],"nameIdentifiers":[]},{"name":"Sirisamphanwong, Chattariya","givenName":"Chattariya","familyName":"Sirisamphanwong","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Transfer learning-based fault detection in wind turbine blades using radar plots and deep learning models"}],"publisher":"Taylor \u0026 Francis","container":{},"publicationYear":2023,"subjects":[{"subject":"Space Science"},{"subject":"Medicine"},{"subject":"Sociology"},{"subject":"FOS: Sociology","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Science Policy"},{"subject":"Mental Health"}],"contributors":[],"dates":[{"date":"2023-09-04","dateType":"Created"},{"date":"2024-02-13","dateType":"Updated"},{"date":"2023","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsIdenticalTo","relatedIdentifier":"10.6084/m9.figshare.24080729","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementTo","relatedIdentifier":"10.1080/15567036.2023.2246400","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["8231251 Bytes"],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Faults in wind turbine blades are considered a critical issue that can affect the safety and performance of wind turbines. The proposed research aimed to monitor wind turbine blades and identify fault conditions using a transfer learning approach. The study utilized one good and four faulty blade conditions: bend, hub-blade loose connection, erosion, and pitch angle twist. Vibration signals for each blade condition were collected and converted as radar plots that were fed and analyzed using pre-trained deep learning models including ResNet-50, AlexNet, VGG-16, and GoogleNet. Hyperparameters including optimizer, train-test split ratio, batch size, epochs, and learning rate were examined to determine the optimal configuration for each network. The study’s core findings indicate that ResNet-50 outperformed all other models, achieving an impressive accuracy rate of 99.00%. The other models achieved lower accuracy rates, with AlexNet achieving 96.70%, GoogleNet achieving 97.00%, and VGG-16 achieving 95.00%. These findings highlight the potential of using deep learning models for wind turbine monitoring and fault detection, which could significantly improve the efficiency and reliability of wind turbines.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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