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GLDAN: Global and Local Domain Adaptation Network for Cross-Wind Turbine Fault Diagnosis
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Operating under harsh conditions and exposed to fluctuating loads for extended periods, wind turbines experience a heightened vulnerability in their key components. Early fault detection is crucial to enhance the reliability ...
Condition Monitoring of Wind Turbines Based on Anomaly Detection Using Deep Support Vector Data Description
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Wind turbine condition monitoring is considered a key task in the wind power industry. A plethora of methodologies based on machine learning have been proposed for monitoring wind turbines, but the absence of faulty data ...