Wind Turbine Blade Bearing Fault Diagnosis Under Fluctuating Speed Operations via Bayesian Augmented Lagrangian Analysis

Blade bearings are joint components of
variable-pitch wind turbines which have high failure rates.
This paper diagnoses a naturally damaged wind turbine blade
bearing which was in operation on a wind farm for over 15
years; therefore, its vibration signals are more in line with field
situations. The focus is placed on the conditions of fluctuating
slow-speeds and heavy loads, because blade bearings bear
large loads from wind turbine blades and their rotation speeds
are sensitively affected by wind loads or blade flipping. To
extract weak fault signals masked by heavy noise, a novel signal
denoising method, Bayesian Augmented Lagrangian (BAL)
Algorithm, is used to build a sparse model for noise reduction.
BAL can denoise the signal by transforming the original
filtering problem into several sub-optimization problems under
the Bayesian framework and these sub-optimization problems
can be further solved separately. Therefore, it requires fewer
computational requirements. After that, the BAL denoised signal
is resampled with the aim of eliminating spectrum smearing
and improving diagnostic accuracy. The proposed framework
is validated by different experiments and case studies. The
comparison with respect to some popular diagnostic methods
is explained in detail, which highlights the superiority of our
introduced framework.