基于增强形态滤波与三阶累积量对角切片谱的风力发电机滚动轴承故障诊断方法

罗园庆; 陈长征; 赵思雨

doi:10.19912/j.0254-0096.tynxb.2020-0577

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太阳能学报 ›› 2022, Vol. 43 ›› Issue (3) : 373-381. DOI: 10.19912/j.0254-0096.tynxb.2020-0577

基于增强形态滤波与三阶累积量对角切片谱的风力发电机滚动轴承故障诊断方法

罗园庆¹, 陈长征^1,2, 赵思雨¹

作者信息 +

WIND TURBINE ROLLING BEARING FAULT DIAGNOSIS METHOD BASED ON ENHANCED MORPHOLOGICAL FILTERING AND THIRD-ORDER CUMULANT DIAGONAL SLICE SPECTRUM

Luo Yuanqing¹, Chen Changzheng^1,2, Zhao Siyu¹

Author information +

文章历史 +

摘要

针对大型风力发电机滚动轴承的故障信号受到强背景噪声干扰不易识别的问题,提出一种基于增强形态滤波与三阶累积量对角切片谱相结合的故障诊断检测方法。该方法首先在研究基本形态学算子的基础上,构建一种新的增强型形态学算子（EMDO）;随后利用特征能量因子（FEF）选择出EMDO算子的最优结构元素尺度;最后利用三阶累积量对角切片谱的消噪性能来进一步增强EMDO算子对风力发电机轴承故障信息的特征提取能力。仿真和对比实验结果表明,所提方法能有效消除高斯白噪生的干扰,对提取风力发电机轴承的故障特征信息起到增强的效果。

Abstract

To solve the problem that the fault signal of the wind turbine rolling bearing is difficult to be identified by the strong background noise, a fault detection method based on the enhanced morphological filtering and the third-order cumulant diagonal slice spectrum is proposed. Firstly, this method constructs a new enhanced morphology operator （EMDO） based on the basic morphology operators. Then, the feature energy factor is used to select the optimal structural element scale of the EMDO operator. Finally, the third-order cumulant diagonal slice spectrum de-noising performance is used to further enhance the feature extraction ability of the EMDO operator. The results of simulation and comparison experiments show that the method proposed in this paper can effectively eliminate the interference caused by Gaussian white noise and enhance the extraction of fault feature information of the wind turbine rolling bearing.

导出引用

罗园庆, 陈长征, 赵思雨. 基于增强形态滤波与三阶累积量对角切片谱的风力发电机滚动轴承故障诊断方法[J]. 太阳能学报. 2022, 43(3): 373-381 https://doi.org/10.19912/j.0254-0096.tynxb.2020-0577

Luo Yuanqing, Chen Changzheng, Zhao Siyu. WIND TURBINE ROLLING BEARING FAULT DIAGNOSIS METHOD BASED ON ENHANCED MORPHOLOGICAL FILTERING AND THIRD-ORDER CUMULANT DIAGONAL SLICE SPECTRUM[J]. Acta Energiae Solaris Sinica. 2022, 43(3): 373-381 https://doi.org/10.19912/j.0254-0096.tynxb.2020-0577

中图分类号： TK83 TH133

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