轴承的振动信号降噪方法基于混合增强驼群算法,多种模式分解和波纹值 (HRCSA-VMD-WT) 综合模型
Zhuang Li1, Xingtian Yao1, Cheng Zhang1
1School of Mechanical Engineering, Nantong University, Nantong 226019, China.
Sensors (Basel, Switzerland)
|June 19, 2024
概括
一种新的混合增强驼群算法-变化模式分解-波纹转换 (HRCSA-VMD-WT) 模型显著降低了旋转轴承振动信号中的噪声. 这种先进的方法提高了故障检测的准确性和机械诊断的效率.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 旋转轴承振动分析对于故障检测至关重要.
- 现有的降噪方法经常与复杂的信号干扰作斗争.
- 有效的信号消噪对于准确的机器诊断至关重要.
研究的目的:
- 开发一个先进的降噪模型,用于旋转轴承的振动信号.
- 为了提高转轴承故障检测的准确性和效率.
- 为信号处理应用引入一种新的优化算法.
主要方法:
- 通过整合混沌反向学习,鱼优化算法和考希突变,开发了混合增强驼群算法 (HRCSA).
- 优化变量模式分解 (VMD) 使用HRCSA提取内在模式函数 (IMF).
- 应用波段值 (WT) 拒绝杂的IMF,并重建了信号.
主要成果:
- 与PSO,WOA和GWO相比,HRCSA展示了优越的融合速度和精度.
- 在HRCSA-VMD-WT的研究中,信号与噪声比 (SNR) 至少提高了74.9%.
- 在HRCSA-VMD-WT的结果中,根平均平方误差 (RMSE) 至少减少了41.2%.
结论:
- 该HRCSA-VMD-WT模型为旋转轴承的振动信号提供了显著的降噪.
- 这种方法提高了故障检测准确性和诊断效率.
- 该研究提供了一个可靠和有效的解决方案,用于旋转轴承的维护.
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