一个基于IPOA-VMD和MOMEDA的滚动轴承故障特征提取算法
Kang Yi1, Changxin Cai1,2, Wentao Tang3
1School of Electronic Information, Yangtze University, Jingzhou 434023, China.
这项研究介绍了一种改进的算法,用于从噪音振动数据中提取滚动轴承故障特征. 该方法通过结合改进的优化算法 (IPOA) 与可变模态分解 (VMD) 和多点最佳最小解卷调整 (MOMEDA) 来增强故障检测.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 机器状态监测 机器状态监测
背景情况:
- 来自滚动轴承的振动信号通常会被显著的背景噪声损坏.
- 准确地提取故障特征对于有效的状态监测和预测性维护至关重要.
- 现有的方法在高噪音水平中难以分离故障标志.
研究的目的:
- 开发一个强大的算法,用于滚动轴承在杂的环境中提取故障特征.
- 提高旋转机械故障诊断的准确性和可靠性.
- 为了增强检测暂时冲击元件,表明轴承故障.
主要方法:
- 使用反向学习策略开发了一种改进的优化算法 (IPOA).
- 应用了可变模态分解 (VMD) 来分解噪声信号.
- 使用多点最佳最小解卷调整 (MOMEDA) 来实现最佳解卷.
- 基尼系数标准选择了最佳的模式组件.
- 泰格能源运营商 (TEO) 负责信号调节和分析.
主要成果:
- 对IPOA的优化性能进行了验证.
- 提出的方法成功地从模拟和实际轴承信号中提取了故障特征.
- 实现了有效增强短暂冲击组件.
- 准确的故障特征提取被证明,即使有强大的背景噪声干扰.
结论:
- 开发的IPOA-VMD-MOMEDA算法为滚动轴承故障诊断提供了卓越的解决方案.
- 该方法有效地减轻了背景噪声对故障特征提取的影响.
- 这种方法显著提高了滚动轴承状况监测的可靠性.
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