基于增强组合差分形态滤波器的大型风力机轴承故障诊断

FAULT DIAGNOSIS OF LARGE-SCALE WIND TURBINE BEARING BASED ON ENHANCED COMBINATION GRADIENT MORPHOLOGICAL FILTER

针对由于大型风力发电机的工作环境恶劣其发电机轴承的故障特征信息会受到强背景噪声和电磁噪声干扰而难以检测的问题,提出一种新的增强组合差分形态滤波器(ECGMF)用于检测滚动轴承的故障特征信息。该方法结合改进的基本形态学算子的特点,构造一种既能保留信号的故障特征,又能抑制噪声干扰的增强组合差分形态学算子。此外,为了解决峭度准则和信噪比在选择结构元素的尺度时不够准确的问题,采用特征频率强度系数(C_(CFI))来自适应优化结构元素的尺度。仿真和实验结果表明,所提出的方法能有效提取风力机轴承故障的特征信息。与其他形态滤波器的对比结果验证该方法的有效性和优越性。

In view of the harsh working environment of large-scale wind turbines,the fault feature information of their generator bearings is difficult to detect due to strong background noise and other interference.In this paper,a new enhanced combination gradient morphological filter(ECGMF)is proposed to detect the fault feature information of rolling bearings.Combined with the characteristics of improved basic morphological operators,the proposed method can not only preserve the fault characteristics of the signal,but also suppress the noise interference.To overcome the scale instability in selecting structure elements based on kurtosis criteria and signal-to-noise ratio,the characteristic frequency intensity coefficient(C_(CFI))is adopted in this paper to select the optimal structure element.Simulation and experimental results show that the proposed method can effectively extract the fault feature information of wind turbine bearing.Compared with other morphological filters,the results show that the proposed method is effective and advantageous.