基于自适应VMD-K-SVD的滚动轴承故障信号增强研究
Meijiao Mao1,2, Kaixin Zeng1, Zhifei Tan1,2
1School of Mechanical Engineering and Mechanics, Xiangtan University, Xiangtan 411105, China.
Sensors (Basel, Switzerland)
|October 28, 2023
概括
本研究提出了一种结合变化模式分解 (VMD) 和K-单值值分解 (K-SVD) 的新方法,以有效地消除滚动轴承信号,提高故障检测的准确性.
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:
- 滚动轴承是旋转机械中的关键部件.
- 用于故障检测的信号分析受到噪声,非静止性和非线性挑战.
- 有效的除对于准确的故障特征提取至关重要.
研究的目的:
- 开发一种新的综合方法来消除和增强滚动轴承信号.
- 为了应对来自噪音和复杂信号的故障特征提取方面的挑战.
- 提高滚动轴承状况监测系统的可靠性.
主要方法:
- 拟议的方法整合了变化模式分解 (VMD) 和K-单数值分解 (K-SVD).
- 一个改进的算术优化算法 (IAOA) 为信号分解优化VMD参数 (K,α).
- K-SVD应用于由此产生的内在模式函数 (IMFs),以进一步降低噪音.
主要成果:
- 结合的VMD-K-SVD方法有效地减少了滚动轴承信号中的噪声.
- 该方法提高了故障特征信号的清晰度,以便更好地分析.
- 使用CWRU和推力轴承试验装置数据的验证证实了该方法的有效性.
结论:
- 新的VMD-K-SVD方法提供了一个强大的解决方案,用于消除滚动轴承信号.
- 这种技术显著改善了从复杂的操作数据中提取故障特征.
- 拟议的方法提高了状态监测和诊断系统的性能.
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