FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4383551437> ?p ?o ?g. }

• W4383551437 endingPage "6198" @default.
• W4383551437 startingPage "6198" @default.
• W4383551437 abstract "Fault alarm time lag is one of the difficulties in fault diagnosis of wind turbine generators (WTGs), and the existing methods are insufficient to achieve accurate and rapid fault diagnosis of WTGs, and the operation and maintenance costs of WTGs are too high. To invent a new method for fast and accurate fault diagnosis of WTGs, this study constructs a stacking integration model based on the machine learning algorithms light gradient boosting machine (LightGBM), extreme gradient boosting (XGBoost), and stochastic gradient descent regressor (SGDRegressor) using publicly available datasets from Energias De Portugal (EDP). This model is automatically tuned for hyperparameters during training using Bayesian tuning, and the coefficient of determination (R2) and root mean square error (RMSE) were used to evaluate the model to determine its applicability and accuracy. The fitted residuals of the test set were calculated, the Pauta criterion (3σ) and the temporal sliding window were applied, and a final adaptive threshold method for accurate fault diagnosis and alarming was created. The model validation results show that the adaptive threshold method proposed in this study is better than the fixed threshold for diagnosis, and the alarm times for the GENERATOR fault type, GENERATOR_BEARING fault type, and TRANSFORMER fault type are 1.5 h, 5.8 h, and 3 h earlier, respectively." @default.
• W4383551437 created "2023-07-08" @default.
• W4383551437 creator A5006028146 @default.
• W4383551437 creator A5010546326 @default.
• W4383551437 creator A5025160903 @default.
• W4383551437 creator A5051233506 @default.
• W4383551437 creator A5061571458 @default.
• W4383551437 creator A5062082438 @default.
• W4383551437 creator A5090101366 @default.
• W4383551437 date "2023-07-06" @default.
• W4383551437 modified "2023-09-27" @default.
• W4383551437 title "Fault Diagnosis of Wind Turbine Generators Based on Stacking Integration Algorithm and Adaptive Threshold" @default.
• W4383551437 cites W1597576211 @default.
• W4383551437 cites W2037088298 @default.
• W4383551437 cites W2048946027 @default.
• W4383551437 cites W2061144551 @default.
• W4383551437 cites W2067610416 @default.
• W4383551437 cites W2072857564 @default.
• W4383551437 cites W2102148524 @default.
• W4383551437 cites W2123162799 @default.
• W4383551437 cites W2137820733 @default.
• W4383551437 cites W2217684237 @default.
• W4383551437 cites W2560760489 @default.
• W4383551437 cites W2755314367 @default.
• W4383551437 cites W2776641310 @default.
• W4383551437 cites W2795411881 @default.
• W4383551437 cites W2889423755 @default.
• W4383551437 cites W2899360057 @default.
• W4383551437 cites W2899924860 @default.
• W4383551437 cites W2909503120 @default.
• W4383551437 cites W2954792909 @default.
• W4383551437 cites W2964103799 @default.
• W4383551437 cites W2964239088 @default.
• W4383551437 cites W2989066261 @default.
• W4383551437 cites W2997477191 @default.
• W4383551437 cites W3009747427 @default.
• W4383551437 cites W3009961868 @default.
• W4383551437 cites W3027451447 @default.
• W4383551437 cites W3037923631 @default.
• W4383551437 cites W3043728443 @default.
• W4383551437 cites W3087179963 @default.
• W4383551437 cites W3091704525 @default.
• W4383551437 cites W3092417622 @default.
• W4383551437 cites W3092601199 @default.
• W4383551437 cites W3102476541 @default.
• W4383551437 cites W3109443211 @default.
• W4383551437 cites W3110023814 @default.
• W4383551437 cites W3126685764 @default.
• W4383551437 cites W3131848660 @default.
• W4383551437 cites W3146685335 @default.
• W4383551437 cites W3157039972 @default.
• W4383551437 cites W317957491 @default.
• W4383551437 cites W3195108282 @default.
• W4383551437 cites W3196854132 @default.
• W4383551437 cites W3205301901 @default.
• W4383551437 cites W3208459708 @default.
• W4383551437 cites W3210180182 @default.
• W4383551437 cites W4205817860 @default.
• W4383551437 cites W4207074816 @default.
• W4383551437 cites W4224259590 @default.
• W4383551437 cites W4240294902 @default.
• W4383551437 cites W4246559677 @default.
• W4383551437 cites W4251708881 @default.
• W4383551437 cites W4296103744 @default.
• W4383551437 cites W4304112139 @default.
• W4383551437 cites W4313503941 @default.
• W4383551437 doi "https://doi.org/10.3390/s23136198" @default.
• W4383551437 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37448048" @default.
• W4383551437 hasPublicationYear "2023" @default.
• W4383551437 type Work @default.
• W4383551437 citedByCount "0" @default.
• W4383551437 crossrefType "journal-article" @default.
• W4383551437 hasAuthorship W4383551437A5006028146 @default.
• W4383551437 hasAuthorship W4383551437A5010546326 @default.
• W4383551437 hasAuthorship W4383551437A5025160903 @default.
• W4383551437 hasAuthorship W4383551437A5051233506 @default.
• W4383551437 hasAuthorship W4383551437A5061571458 @default.
• W4383551437 hasAuthorship W4383551437A5062082438 @default.
• W4383551437 hasAuthorship W4383551437A5090101366 @default.
• W4383551437 hasBestOaLocation W43835514371 @default.
• W4383551437 hasConcept C11413529 @default.
• W4383551437 hasConcept C127313418 @default.
• W4383551437 hasConcept C127413603 @default.
• W4383551437 hasConcept C153258448 @default.
• W4383551437 hasConcept C154945302 @default.
• W4383551437 hasConcept C165205528 @default.
• W4383551437 hasConcept C169258074 @default.
• W4383551437 hasConcept C175551986 @default.
• W4383551437 hasConcept C2775924081 @default.
• W4383551437 hasConcept C2778449969 @default.
• W4383551437 hasConcept C41008148 @default.
• W4383551437 hasConcept C47446073 @default.
• W4383551437 hasConcept C50644808 @default.
• W4383551437 hasConcept C70153297 @default.
• W4383551437 hasConcept C78519656 @default.
• W4383551437 hasConceptScore W4383551437C11413529 @default.
• W4383551437 hasConceptScore W4383551437C127313418 @default.
• W4383551437 hasConceptScore W4383551437C127413603 @default.

• next