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

• Published in: IEEE transactions on industrial informatics Vol. 17; no. 7; pp. 4613 - 4623
• Main Authors: Liu, Zepeng, Tang, Xiaoquan, Wang, Xuefei, Mugica, Jose Errea, Zhang, Long
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.07.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Bayes methods; Bayesian analysis; Bayesian augmented Lagrangian (BAL); Bearings; blade bearing fault diagnosis; Blades; Diagnostic systems; Failure rates; Fault diagnosis; Filtering; Galling; Load modeling; Noise reduction; Signal denoising; slow-speed bearing; Turbine blades; vibration signal analysis; Wind damage; Wind loads; Wind power; Wind turbines
• ISSN: 1551-3203; 1941-0050; 1941-0050
• DOI: 10.1109/TII.2020.3012408

Blade bearings are joint components of variable-pitch wind turbines, which have high failure rates. This article 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 suboptimization problems under the Bayesian framework and these suboptimization 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.