针对单一振动信号或电流信号估计风电机组瞬时频率精度不高的问题,提出基于振动信号和转子电流信号深度融合的转频估计及齿轮箱故障诊断方法,旨在提高风电机组齿轮箱故障诊断的准确性。首先,分别利用变分模态分解和低通滤波对振动信号和转子电流信号进行降噪处理,通过同步提取变换分别获得振动信号和转子电流信号的瞬时频率曲线,并对两种信号的时频曲线进行预处理,获得两条风电机组时频曲线;然后,提出DA-BLCNN深度网络模型,融合得到齿轮箱故障轴承的瞬时频率曲线;最后,采用阶次跟踪算法对齿轮箱振动信号进行等角度重采样,实现对齿轮箱的故障检测。变转速工况下的齿轮箱轴承故障实验验证结果表明,所提方法可有效实现齿轮箱故障诊断。
Abstract
To address the issue of low accuracy in estimating the instantaneous frequency of wind turbines using a single vibration or current signal, this paper proposes a method for rotational frequency estimation and gearbox fault diagnosis based on the deep integration of vibration and rotor current signals. The goal is to improve the accuracy of gearbox fault diagnosis in wind turbines. Firstly, the vibration and rotor current signals are denoised using the Variational Mode Decomposition method and low-pass filtering, respectively. The instantaneous frequency curves of the vibration and rotor current signals are then obtained using synchroextracting transform, and the time-frequency curves of both signals are preprocessed to obtain two time-frequency curves of the wind turbine. Subsequently, a DA-BLCNN deep network model is proposed to fuse the instantaneous frequency curves of the gearbox fault bearing. Finally, an order tracking algorithm is used to resample the gearbox vibration signal at equal angles, enabling fault detection of the gearbox. Through experimental validation of bearing faults in the gearbox under varying speeds, it can be concluded that the method proposed in this paper can effectively achieve fault diagnosis of the gearbox.
关键词
风电机组 /
齿轮箱 /
数据融合 /
转子电流 /
故障诊断 /
转频估计
Key words
wind turbines /
gearbox /
data fusion /
rotor current /
fault diagnosis /
frequency estimation
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基金
国家自然科学基金（52275109）; 河北省自然科学基金（E2022502007）; 河北省研究生创新能力培养资助项目（CXZZBS2025196）
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