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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.

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To harvest wind energy from nature, wind turbines are increasingly installed globally, and the blades are the most essential components within the turbine system. The blades usually suffer from time-varying non-stationary wind loads, and the load information is normally unknown or difficult to collect. This poses significant challenges to the blade assessment and damage detection. Transmissibility Function (TF) methods have the potential to address this challenge as they do not require loading information. In this paper, a novel Wavelet Package Energy TF (WPETF) method is proposed to increase the high frequency resolution while maintaining its low sensitivity to noise and it is further used for wind turbine blade fault detection. Compared with the existing Fourier TF (FTF) method, the proposed method is immune to the external loading impacts, does not require excitation knowledge, and is robust to noise. Compared with the existing Wavelet Energy TF (WETF) method, the novel one uses Wavelet Package Decomposition (WPD) instead of Wavelet Decomposition (WD) to further increase the high frequency resolution which provides richer damage-induced information. The effectiveness of the WPETF method for wind turbine blade condition assessment is first verified numerically and then on three industrial-scale wind turbine blades with both naturally (uncontrolled) and artificially-introduced (controlled) damage. Its advantages over a number of existing methods are also demonstrated.

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