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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 ...
Fleet Based Monitoring With Multi-Feature Hierarchical Clustering
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Abstract. Fleet-wide condition monitoring is a well-known method for continuously assessing the operational health of an entire fleet, ensuring efficient and reliable performance. With the emergence of fleet-based monitoring, ...
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 ...
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