The bearing vibration of wind turbines is nonlinear and non-stationary. To effectively extract
bearing vibration signal features for fault diagnosis, a method of feature vector extraction …

Vibration signal of wind turbine has the non‐linear and non‐stationary characteristic, thus it
is difficult to extract the fault feature. In this study, a novel method based on variational mode …

Abstract Machine learning techniques have been successfully applied in intelligent fault
diagnosis of rolling bearings in recent years. However, in the real world industrial …

As a powerful tool of mode component extraction, variational mode decomposition (VMD)
has found its wide application in fault diagnosis. However, VMD still suffers from an …

Aimed at the problem that the signal data of wind turbine faulty gearbox is difficult to obtain
and the health condition is difficult to diagnose under variable working conditions, a fault …

The bearing fault diagnosis is of vital significance in maintaining the safety of rotation
machine. Among various fault detection techniques, the diagnosis based on vibration signal …

Diagnosis of bearing faults has significant meaning to the maintenance of wind turbines in
real industry. Well-performed bearing fault diagnosis generally requires effective features …

Variational mode decomposition (VMD) is a newly proposed signal processing method
which is diffusely used in fault diagnosis of rotating equipment. It has two issues that need to …

Due to the fact that measured vibration signals from a bearing are complex and non-
stationary in nature, and that impulse characteristics are always immersed in stochastic …

According to the non-stationary characteristics of roller bearing fault vibration signals, a
roller bearing fault diagnosis method based on empirical mode decomposition (EMD) …