FAULT DIAGNOSIS OF WIND TURBINE MAIN BEARINGS BASED ON HARMONIC REMOVAL AND SPECTRAL KURTOSIS

Chen Qiang; Qian Xun

doi:10.19912/j.0254-0096.tynxb.2024-0582

PDF(2242 KB)

Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (1) : 1-8. DOI: 10.19912/j.0254-0096.tynxb.2024-0582

FAULT DIAGNOSIS OF WIND TURBINE MAIN BEARINGS BASED ON HARMONIC REMOVAL AND SPECTRAL KURTOSIS

Chen Qiang, Qian Xun

Author information +

History +

Abstract

Aiming at the problems that the fault features of the main bearings are interfered by the harmonics of other transmission components in wind turbines' condition monitoring and the raw vibration signal is non-stationary, making it difficult to accurately extract the characteristic frequency of fault impacts, a combined method of harmonic removal and spectral kurtosis analysis （HR-Fast-Kurtogram） is proposed. Offline angle domain resampling is used to convert non-stationary time domain signals into stationary angle-order signals. Comb notch filters are introduced in the order domain to remove gear meshing frequencies, and Fast-Kurtogram based on short-time Fourier transfer （STFT） is applied to extract the fault sub-bands where the kurtosis peak is located in order to extract bearing fault features. The combined method is able to avoid the interference of non-periodic impacts and distinguish between non-bearing amplitude modulation components with lower impact and bearing fault impact components. The comparative analysis results in the application of real main bearing fault data shows that the proposed method can improve the accuracy of fault features extraction and fault type identification in a short time scale of seconds.

Key words

wind turbines / bearings / condition monitoring / notch filters / spectral kurtosis

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Chen Qiang, Qian Xun. FAULT DIAGNOSIS OF WIND TURBINE MAIN BEARINGS BASED ON HARMONIC REMOVAL AND SPECTRAL KURTOSIS[J]. Acta Energiae Solaris Sinica. 2025, 46(1): 1-8 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0582

References

[1] 苏连成, 李兴林, 李小俚, 等. 风电机组轴承的状态监测和故障诊断与运行维护[J]. 轴承, 2012(1): 47-53.
SU L C, LI X L, LI X L, et al.Condition monitoring and fault diagnosis and operating maintenance systems for wind turbine bearings[J]. Bearing, 2012(1): 47-53.
[2] 安学利, 蒋东翔, 刘超, 等. 基于固有时间尺度分解的风电机组轴承故障特征提取[J]. 电力系统自动化, 2012, 36(5): 41-44, 102.
AN X L, JIANG D X, LIU C, et al.Bearing fault feature extraction of wind turbine based on intrinsic time-scale decomposition[J]. Automation of electric power systems, 2012, 36(5): 41-44, 102.
[3] 赵洪山, 李浪, 王颖. 一种基于盲源分离和流形学习的风电机组轴承故障特征提取方法[J]. 太阳能学报, 2016, 37(2): 269-275.
ZHAO H S, LI L, WANG Y.Fault feature extraction method of wind turbine bearing based on blind source separation and manifold learning[J]. Acta energiae solaris sinica, 2016, 37(2): 269-275.
[4] 孟良, 苏元浩, 许同乐, 等. 并行卷积神经网络的风电机组故障诊断方法[J]. 太阳能学报, 2023, 44(5): 449-456.
MENG L, SU Y H, XU T L, et al.Wind turbine fault diagnosis method based on parallel convolutional neural network[J]. Acta energiae solaris sinica, 2023, 44(5): 449-456.
[5] 彭进, 王维庆, 王海云, 等. 基于EEMD峭度-相关系数准则的多特征量风电机组轴承故障诊断[J]. 可再生能源, 2016, 34(10): 1481-1490.
PENG J, WANG W Q, WANG H Y, et al.Fault diagnosis method of wind turbine's bearing based on EEMD kurtosis-correlation coefficients criterion and multiple features[J]. Renewable energy resources, 2016, 34(10): 1481-1490.
[6] 刘洋, 程强, 史曜炜, 等. 基于注意力模块及1D-CNN的滚动轴承故障诊断[J]. 太阳能学报, 2022, 43(3): 462-468.
LIU Y, CHENG Q, SHI Y W, et al.Fault diagnosis of rolling bearings based on attention module and 1D-CNN[J]. Acta energiae solaris sinica, 2022, 43(3): 462-468.
[7] 胡超, 沈宝国, 谢中敏. 基于EMD-FastICA与DGA-ELM网络的轴承故障诊断方法[J]. 太阳能学报, 2021, 42(10): 208-219.
HU C, SHEN B G, XIE Z M.Fault diagnosis method of bearing based on EMD-FastICA and DGA-ELM network[J]. Acta energiae solaris sinica, 2021, 42(10): 208-219.
[8] DWYER R.Detection of non-Gaussian signals by frequency domain Kurtosis estimation[C]//ICASSP'83. IEEE International Conference on Acoustics, Speech, and Signal Processing. Boston, MASS, USA, 1983.
[9] ANTONI J.Fast computation of the kurtogram for the detection of transient faults[J]. Mechanical systems and signal processing, 2007, 21(1): 108-124.
[10] 张龙, 毛志德, 杨世锡, 等. 基于包络谱带通峭度的改进谱峭度方法及在轴承诊断中的应用[J]. 振动与冲击, 2018, 37(23): 171-179.
ZHANG L, MAO Z D, YANG S X, et al.An improved Kurtogram based on band-pass envelope spectral Kurtosis with its application in bearing fault diagnosis[J]. Journal of vibration and shock, 2018, 37(23): 171-179.
[11] 代士超, 郭瑜, 伍星, 等. 基于子频带谱峭度平均的快速谱峭度图算法改进[J]. 振动与冲击, 2015, 34(7): 98-102, 108.
DAI S C, GUO Y, WU X, et al.Improvement on fast kurtogram algorithm based on sub-frequency-band spectral kurtosis average[J]. Journal of vibration and shock, 2015, 34(7): 98-102, 108.
[12] ANTONI J.The spectral kurtosis: a useful tool for characterising non-stationary signals[J]. Mechanical systems and signal processing, 2006, 20(2): 282-307.