基于FWECS-CYCBD的轴承故障特征提取研究
作者:
作者单位:

昆明理工大学机电工程学院 昆明,650500

通讯作者:

刘韬,男,1980年3月生,博士、教授、博士生导师。主要研究方向为现代信号处理理论与方法、故障特征提取中的应用、智能故障诊断及设备状态监测和寿命预测。 E-mail:kmliutao@aliyun.com

中图分类号:

TH133.33;TP206+.3

基金项目:

云南省科技厅重大科技专项资助项目(202102AC080002);国家自然科学基金资助项目(52065030)

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    摘要:

    针对最大二阶循环平稳盲解卷积(maximum second-order cyclostationary blind deconvolution,简称CYCBD)特征提取中循环频率和滤波带宽难确定的问题,引入频率加权能量相关谱(frequency weighted energy correlation spectrum,简称FWECS)来改进CYCBD,实现了低信噪比条件下的滚动轴承故障特征提取。首先,通过FWECS获取周期冲击频率,构造循环频率集;其次,利用最大加权谐波显著性指标设计了一种等步长搜索策略,自适应选取滤波器长度;最后,基于优选的循环频率和滤波带宽进行CYCBD解卷积。轴承仿真和实验数据表明:在循环频率等先验信息未知的情况下,FWECS-CYCBD对故障信号中的微弱冲击特征更敏感;与最小熵解卷积、改进最大相关峭度解卷积和自适应最大二阶循环平稳盲解卷积等方法相比,所提方法在低信噪比条件下能较好地提取轴承故障特征频率信息。

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  • 收稿日期:2022-02-16
  • 最后修改日期:2022-05-10
  • 在线发布日期: 2024-10-15
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