Predictive maintenance (PdM) uses statistical and machine learning methods to detect and
predict the onset of faults. PdM is often used in industrial IoT settings in the energy sector …

The main goal of this paper is to review and evaluate how we can take advantage of state-of-
the-art machine learning techniques and apply them in wind energy operation conditions …

We propose a method, a model, and a form of presenting model results for condition
monitoring of a small set of wind turbines with rare failures. The main new ingredient of the …

The rapidly increasing installed capacity of Wind Turbines (WTs) worldwide emphasizes the
need for Operation and Maintenance (O&M) strategies favoring high availability, reliability …

In recent years, there has been growing interest in digital twin technology in both industry
and academia. This versatile technology has found applications across various industries …

Fault diagnostic methods with fuzzy logic methods, SVM, KNN and artificial intelligence
systems have been used in complex systems such as wind turbines, gas turbines, power …

Early fault detection plays a crucial role in the field of predictive maintenance for wind
turbines, yet the comparison of different algorithms poses a difficult task because domain …

Permanently excited synchronous motors are the driving components in countless systems
and applications. The most common cause of motor failures are the bearings. Data-driven …

Anomaly detection plays a crucial role in the field of predictive maintenance for wind
turbines, yet the comparison of different algorithms poses a difficult task because domain …

The last few years have been marked by the transition of the world energy matrix,
predominantly with wind and solar sources considered clean energies. Wind turbines …