Conventional damage detection techniques are gradually being replaced by state-of-the-art
smart monitoring and decision-making solutions. Near real-time and online damage …

Condition monitoring in wind turbines aims at detecting incipient faults at an early stage to
improve maintenance. Artificial neural networks are a tool from machine learning that is …

The complex and changeable working environment of wind turbine often challenges the
condition monitoring and fault detection. In this paper, a new method is proposed for fault …

Wind turbines are widely installed as the new source of cleaner energy production. Dynamic
and random stress imposed on the generator bearing of a wind turbine may lead to …

An image recognition model based on a deep learning network is proposed for the
automatic extraction of image features and the accurate and efficient detection of wind …

Data-driven condition monitoring reduces downtime of wind turbines and increases
reliability. Wind turbine operation and maintenance (O&M) cost is a significant factor that …

This paper proposes and tests a sequence-based modeling of deep learning (DL) for
structural damage detection of floating offshore wind turbine (FOWT) blades using Long …

Data-driven approaches have gained increasing interests in the fault detection of wind
turbines (WTs) due to the difficulty in system modeling and the availability of sensor data …

Aimed at identifying the health state of wind turbines accurately by comprehensively using
the change information in spatial and temporal scale of the supervisory control and data …

Condition monitoring of wind turbines is critical to ensure their long-term stable operation.
With the benefit of deep learning techniques, WTs' health status information can be mined …