基于ICFO-SVMD和改进的波段值的振动信号否定方法
Yanping Cui1, Xiaoxu He1, Zhe Wu1
1School of Mechanical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
一种新的关节消噪方法有效地消除了旋转机械振动信号的噪声. 这种方法通过保留弱故障特征来提高诊断可靠性,从而改善机械健康监测.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 计算智能是一种计算智能.
背景情况:
- 旋转的机器产生非静止的,多元件的振动信号.
- 强大的背景噪音往往掩盖了关键故障特征,降低了诊断准确度.
- 现有的无声化方法与复杂的噪声干扰作斗争.
研究的目的:
- 为受污染的振动信号开发一种先进的关节消噪方法.
- 为了提高旋转机械故障诊断的可靠性.
- 为了提高对弱故障信号的检测.
主要方法:
- 一种混合方法,结合了改进的皮质菌优化算法 (ICFO),连续变化模式分解 (SVMD) 和波形值.
- 通过使用增强的初始化和突变策略,ICFO通过自适应优化SVMD参数.
- 信号分解,使用皮尔森相关性进行组件分类,以及用于重建的自适应波纹值.
主要成果:
- 拟议的方法显著改善了信号与噪声比 (SNR) 并减少了各种噪声级别的根平均平方误差 (RMSE).
- 在模拟中,与VMD,SVMD,VMD-WTD,CFO-SVMD和WTD相比,实现了更高的性能.
- 在真实世界的轴承和变速箱数据上证明了有效性,保留了低剩余方差的故障频率.
结论:
- 联合无声化方法为分析旋转机械中的噪音振动信号提供了强大的解决方案.
- 它有效地抑制噪音,同时保留必要的故障信息,用于准确的诊断.
- 这种技术提高了早期故障检测和预测性维护的潜力.
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