滚动轴承的弱故障特征提取方法基于波纹包分解和优化的MCKD
A Jinbao Yao1,2, B Yizhu Wang2, C Bohao Yue2
1State Key Lab of Mechanical Transmission, Chongqing University, Chongqing 400030, China.
The Review of scientific instruments
|December 26, 2024
概括
这项研究引入了一种用于早期检测滚动轴承故障的新方法. 该技术有效地消除噪音,并增强弱故障信号,以改善诊断.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 状态监控 状态监控
背景情况:
- 由于信号特征较弱,滚动轴承的早期故障检测具有挑战性,特别是在杂的环境中.
- 现有的方法难以提取微妙的故障信息,妨碍及时维护并增加故障风险.
研究的目的:
- 提出一种有效的方法,用于在噪音条件下提取滚动轴承的弱故障特征.
- 为了提高滚动轴承故障诊断的早期检测能力.
主要方法:
- 波段包分解 (WPD) 用于信号分解和降噪.
- 使用皮尔森相关系数和能量比率进行信号重建.
- 多向优化器 (MVO) 算法,以优化最大相关性曲解解卷变 (MCKD) 参数 (L和M).
- 对最终故障特征提取进行信封频谱分析.
主要成果:
- 拟议的WPD-MVO-MCKD方法成功地消除了来自滚动轴承振动信号的噪声.
- 弱故障特征得到了显著的增强,使得更早,更准确的故障检测.
- 模拟和实验结果验证了拟议技术的有效性.
结论:
- WPD-MVO-MCKD方法为滚动轴承中早期弱故障特征提取提供了强大的解决方案.
- 这种方法提高了旋转机械状态监控系统的可靠性.
- 这项研究有助于推进机械故障检测的诊断技术.
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