ArticleSensors (Basel, Switzerland)2020
Wind Turbine Gearbox Condition Monitoring Based on Class of Support Vector Regression Models and Residual Analysis.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Spatio-Temporal Joint Network for Coupler Anomaly Detection Under Complex Working Conditions Utilizing Multi-Source Sensors.Sensors (Basel, Switzerland) · 2026Article
- Audible Noise Evaluation in Wind Turbines Through Artificial Intelligence Techniques.Sensors (Basel, Switzerland) · 2025Article
- An Adaptive Deconvolution Method with Improve Enhanced Envelope Spectrum and Its Application for Bearing Fault Feature Extraction.Sensors (Basel, Switzerland) · 2024Article
- Wind Turbine Condition Monitoring Using the SSA-Optimized Self-Attention BiLSTM Network and Changepoint Detection Algorithm.Sensors (Basel, Switzerland) · 2023Article
- Development of a Smartphone-Based Expert System for COVID-19 Risk Prediction at Early Stage.Bioengineering (Basel, Switzerland) · 2022Article
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Authors and funding
5 authors.
Funding
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Abstract
The intelligent condition monitoring of wind turbines reduces their downtime and increases reliability. In this manuscript, a feature selection-based methodology that essentially works on regression models is used for identifying faulty scenarios. Supervisory control and data acquisition (SCADA) data with 1009 samples from one year and one month before failure are considered. Gearbox oil and bearing temperatures are treated as target variables with all the other variables used for the prediction model. Neighborhood component analysis (NCA) as a feature selection technique is employed to select the best features and prediction performance for several machine learning regression models is assessed. The results reveal that twin support vector regression (99.91%) and decision trees (98.74%) yield the highest accuracy for gearbox oil and bearing temperatures respectively. It is observed that NCA increases the accuracy and thus reliability of the condition monitoring system. Furthermore, the residuals from the class of support vector regression (SVR) models are tested from a statistical point of view. Diebold-Mariano and Durbin-Watson tests are carried out to establish the robustness of the tested models.
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Registered trials
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