多尺度形态学的风电轴承故障诊断研究

Research on fault diagnosis of wind turbine bearing based on multi-scalemorphology

为了能够从大型风力发电机提取滚动轴承的微弱故障特征,本文提出一种名为MEDO的多尺度形态学分析方法。首先本文应用四种基本的形态学算子构造了名为EDO的形态差分算子,该算子能够提取轴承的故障冲击信号。随后,为了解决传统单尺度形态学处理信号包含故障信息不足的问题,本文提出了多尺度形态学分析方法,并采用TEK衡量指标来选择最优的结构元素尺度。最后通过实验和对比结果表明,该算法在检测大型风力发电机滚动轴承故障上的有效性和优越性。

In order to be able to extract the weak fault characteristics of rolling bearings from large wind turbines,this paper proposes a multi-scale morphological analysis method called MEDO.First of all,the paper uses four basic morphological operators to construct a morphological difference operator named EDO,which can extract the fault impact signal of the bearing.Subsequently,in order to solve the problem of insufficient fault information in traditional single-scale morphological processing signals,the paper proposes a multi-scale morphological analysis method,and uses TEK metrics to select the optimal structural element scale.Finally,the experimental and comparative results show that the proposed algorithm is effective and superior in detecting the failure of rolling bearing of large wind turbine.