基于AFMD和SVDD的风电机组变桨轴承损伤识别

王晓龙; 张博文; 金韩微; 付锐棋; 杨秀彬; 吴鹏

doi:10.19912/j.0254-0096.tynxb.2023-1743

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太阳能学报 ›› 2025, Vol. 46 ›› Issue (3) : 514-523. DOI: 10.19912/j.0254-0096.tynxb.2023-1743

基于AFMD和SVDD的风电机组变桨轴承损伤识别

王晓龙¹, 张博文¹, 金韩微¹, 付锐棋¹, 杨秀彬¹, 吴鹏²

作者信息 +

DAMAGE IDENTIFICATION OF WIND TURBINE PITCH BEARING BASED ON AFMD AND SVDD

Wang Xiaolong¹, Zhang Bowen¹, Jin Hanwei¹, Fu Ruiqi¹, Yang Xiubin¹, Wu Peng²

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文章历史 +

摘要

针对风电机组变桨轴承损伤识别问题，提出基于自适应特征模态分解和奇异值分解降噪的损伤识别方法。该方法首先利用龙格库塔优化策略对特征模态分解算法中的频带数量及滤波器长度参数进行搜索，确定最优参数组合后对原始振动信号进行自适应特征模态分解，从中提取出蕴含丰富特征信息的模态分量；继而计算出所提取模态分量的包络信号并做进一步奇异值分解降噪处理，从而增强包络信号的信噪比；最后对比理论损伤特征频率及包络谱中幅值突出的频率成分，用于判断变桨轴承的故障损伤。实验数据分析结果表明，所提方法能从复杂原始振动信号中有效提取出微弱特征信息，实现变桨轴承损伤部位的准确甄别，具有一定工程参考借鉴价值。

Abstract

Aiming at the problem of damage identification of pitch bearings of wind turbine, a damage identification method based on adaptive feature mode decomposition and singular value decomposition denoising is proposed. Firstly, Runge kutta optimization is used to search for the frequency band number and the filter length parameters of the feature mode decomposition algorithm, then the optimal combination of influencing parameters is determined and the original vibration signal is processed by adaptive feature mode decomposition, and the mode component containing rich feature information is extracted. Subsequently, the envelope signal of the extracted mode component is calculated and the singular value decomposition denoising is further operated to enhance the signal-to-noise ratio of the envelope signal. Finally, the theoretical damage characteristic frequency and the frequency component with prominent amplitude in the envelope spectrum are compared to judge the fault damage of the pitch bearing. Experimental data analysis results show that the proposed method can effectively extract weak feature information from complex original vibration signal and accurately identify the damage part of pitch bearing, which has certain engineering reference value.

导出引用

王晓龙, 张博文, 金韩微, 付锐棋, 杨秀彬, 吴鹏. 基于AFMD和SVDD的风电机组变桨轴承损伤识别[J]. 太阳能学报. 2025, 46(3): 514-523 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1743

Wang Xiaolong, Zhang Bowen, Jin Hanwei, Fu Ruiqi, Yang Xiubin, Wu Peng. DAMAGE IDENTIFICATION OF WIND TURBINE PITCH BEARING BASED ON AFMD AND SVDD[J]. Acta Energiae Solaris Sinica. 2025, 46(3): 514-523 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1743

中图分类号： TH17

参考文献

[1] 孙欣宇, 向东, 丁伟, 等. 基于麻雀搜索算法的风电机组独立变桨控制研究[J]. 太阳能学报, 2023, 44(10): 266-274.
SUN X Y, XIANG D, DING W, et al.Research on individual pitch control of wind turbine based on sparrow search algorithm[J]. Acta energiae solaris sinica, 2023, 44(10): 266-274.
[2] 马振国, 邓巍, 赵勇, 等. 风力机变桨轴承故障诊断的冲击链检测法[J]. 机械科学与技术, 2020, 39(9): 1426-1431.
MA Z G, DENG W, ZHAO Y, et al.Shock-chain detection method for fault diagnosis of wind turbine pitch bearing[J]. Mechanical science and technology for aerospace engineering, 2020, 39(9): 1426-1431.
[3] 李婷, 付德义, 薛扬. 基于AE与STFT的变桨轴承裂纹诊断研究[J]. 振动测试与诊断, 2021, 41(2): 299-303, 412.
LI T, FU D Y, XUE Y.Research on crack diagnosis of pitch bearing based on AE and STFT[J]. Journal of vibration, measurement & diagnosis, 2021, 41(2): 299-303, 412.
[4] 赵阳, 陈捷, 洪荣晶, 等. EMD和独立分量分析在转盘轴承故障诊断中的应用[J]. 轴承, 2015(7): 54-59.
ZHAO Y, CHEN J, HONG R J, et al.Application of EMD and ICA in fault diagnosis for slewing bearings[J]. Bearing, 2015(7): 54-59.
[5] 杨杰, 陈捷, 洪荣晶, 等. PCA和自相关包络分析在转盘轴承故障诊断中的应用[J]. 轴承, 2014(10): 54-58.
YANG J, CHEN J, HONG R J, et al.Application of PCA and autocorrelation envelope analysis in fault diagnosis for slewing bearings[J]. Bearing, 2014(10): 54-58.
[6] 王晓龙, 唐贵基, 何玉灵, 等. 变转速工况下基于改进奇异谱分解和1.5维包络阶次谱的风电机组轴承损伤识别[J]. 太阳能学报, 2021, 42(1): 240-247.
WANG X L, TANG G J, HE Y L, et al.Injury identification of wind turbine bearing based on ISSD and 1.5 dimension envelope order spectrum under variable rotating speed condition[J]. Acta energiae solaris sinica, 2021, 42(1): 240-247.
[7] MIAO Y H, ZHANG B Y, LI C H, et al.Feature mode decomposition: new decomposition theory for rotating machinery fault diagnosis[J]. IEEE transactions on industrial electronics, 2023, 70(2): 1949-1960.
[8] AHMADIANFAR I, HEIDARI A A, GANDOMI A H, et al.RUN beyond the metaphor: an efficient optimization algorithm based on Runge Kutta method[J]. Expert systems with applications, 2021, 181: 115079.
[9] 张龙, 熊国良, 黄文艺. 复小波共振解调频带优化方法和新指标[J]. 机械工程学报, 2015, 51(3): 129-138.
ZHANG L, XIONG G L, HUANG W Y.New procedure and index for the parameter optimization of complex wavelet based resonance demodulation[J]. Journal of mechanical engineering, 2015, 51(3): 129-138.
[10] 王丽婕, 刘田梦, 王勃, 等. 基于奇异值分解与卡尔曼滤波修正多位置NWP的短期风电功率预测[J]. 太阳能学报, 2022, 43(12): 392-398.
WANG L J, LIU T M, WANG B, et al.Short-term wind power prediction based on SVD and Kalman filter correction of multi-position NWP[J]. Acta energiae solaris sinica, 2022, 43(12): 392-398.
[11] 王海山. 轴对称静电场电位导数计算的数值方法探究[J]. 长春光学精密机械学院学报, 1994, 17(3): 23-27.
WANG H S.Research on the numerical methods for calculating the derivatives of electropotential of an axisymmetrical electrostatic field[J]. Journal of Changchun Institute of Optics and Fine Mechanics, 1994, 17(3): 23-27.
[12] 陈剑, 刘圆圆, 黄凯旋, 等. 基于奇异值分解和独立分量分析的滚动轴承故障诊断方法[J]. 计量学报, 2022, 43(6): 777-785.
CHEN J, LIU Y Y, HUANG K X, et al.Rolling bearing fault diagnosis method based on singular value decomposition and independent component analysis[J]. Acta metrologica sinica, 2022, 43(6): 777-785.
[13] DYBAŁA J. Diagnosing of rolling-element bearings using amplitude level-based decomposition of machine vibration signal[J]. Measurement, 2018, 126: 143-155.
[14] PANG B, NAZARI M, TANG G J.Recursive variational mode extraction and its application in rolling bearing fault diagnosis[J]. Mechanical systems and signal processing, 2022, 165: 108321.
[15] 杨栋, 黄民, 马超. 基于WOA-VMD和快速谱峭度的轴承故障诊断[J]. 北京信息科技大学学报(自然科学版), 2023, 38(2): 16-22.
YANG D, HUANG M, MA C.Bearing fault diagnosis based on WOA-VMD and fast spectral kurtosis[J]. Journal of Beijing Information Science & Technology University, 2023, 38(2): 16-22.
[16] 王振亚, 伍星, 刘韬, 等. 奇异谱分解联合互信息的主轴轴承故障特征提取研究[J]. 振动与冲击, 2023, 42(15): 23-30, 47.
WANG Z Y, WU X, LIU T, et al.Fault feature extraction of spindle bearing based on SSD and MI[J]. Journal of vibration and shock, 2023, 42(15): 23-30, 47.
[17] 李妍, 赵艳杰, 李怀金. 一种SSB信号频域信噪比估计算法[J]. 通信技术, 2009, 42(3): 19-21.
LI Y, ZHAO Y J, LI H J.An algorithm of SNR frequency-domain estimation for SSB signals[J]. Communications technology, 2009, 42(3): 19-21.