摘要
针对风电机组偏航轴承损伤识别问题,提出了基于强化双树复小波包变换的损伤识别方法。首先,通过双树复小波包变换与线性峭度结合对不同分解层数下的分量计算平均线性峭度值,确定最优分解层数;其次,对最优分解所得小波系数及尺度系数进行幅值调制,进而增强不同信号成分的能量;然后,采用散布熵指标确定各分量最佳调制系数并通过双树复小波包逆变换得到修正信号;最后,对修正信号作归一化平方包络谱分析提取故障特征频率。结果表明:所提方法能够实现复杂工况下偏航轴承损伤类型的准确识别,具有一定工程参考价值。
Aiming at the problem of yaw bearing wind turbine damage identification,a damage identification method based on enhanced dual tree complex wavelet packet transform was proposed.Firstly,to determine the number of optimal decomposition layers,components average L-kurtosis values of different decomposition layers were calculated by combining dual tree complex wavelet packet transform and L-kurtosis.Secondly,wavelet coefficient and scale coefficient obtained by the optimal decomposition were modulated to enhance the energy of different signal components.Thirdly,the optimum modulation coefficients of each component were determined by dispersion entropy index,and the modified signal was obtained by inversion dual tree complex wavelet packet.Finally,the normalized square envelope spectrum of modified signal was used to extract the damage characteristic frequency.Results show that the proposed method can accurately identify the damage type of yaw bearing under complex working conditions,and has certain engineering reference value.
作者
王晓龙
金韩微
张博文
石海超
杨秀彬
何玉灵
WANG Xiaolong;JIN Hanwei;ZHANG Bowen;SHI Haichao;YANG Xiubin;HE Yuling(Department of Mechanical Engineering,North China Electric Power University,Baoding 071000,Hebei Province,China;Hebei Key Laboratory of Electric Machinery Health Maintenance and Failure Prevention,North China Electric Power University,Baoding 071000,Hebei Province,China)
出处
《动力工程学报》
北大核心
2025年第1期115-123,共9页
Journal of Chinese Society of Power Engineering
基金
国家自然科学基金资助项目(52005180)
河北省自然科学基金资助项目(E2022502003)
河北省高等学校科学技术研究资助项目(QN2022190)
中央高校基本科研业务费专项资金资助项目(2023MS127)。
关键词
风电机组
偏航轴承
双树复小波包变换
谱幅值调制
wind turbine
yaw bearing
dual tree complex wavelet packet transform
spectral amplitude modulation
