改进的经验模式分解方法,基于旋信号的振幅频率特征
Jie Chen1, Fu-Rong Tian1, Bin Li1
1School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, China.
这项研究引入了一种改进的实证模式分解 (EMD) 方法,从噪音流量计数据中提取信号. 改进的技术通过过和自适应分解信号,提高了准确性,特别是在低流速时.
科学领域:
- 流体动力学 流体动力学
- 信号处理 信号处理
- 仪器仪表工程 仪器仪表工程
背景情况:
- 流量计对于流量测量至关重要,但容易受到噪声干扰,影响准确性.
- 提取精确的信号,特别是在低流速时,仍然是一个重大的挑战,因为噪声复杂.
- 经验模式分解 (EMD) 是一种适用于非线性,非静止信号的时频分析方法,例如来自流量计的信号.
研究的目的:
- 开发一种改进的经验模式分解 (EMD) 方法,用于精确的信号提取.
- 提高流量计的抗干扰能力和精度,特别是在噪音条件下.
- 为了应对在低流速下从复杂的噪声中提取弱信号的挑战.
主要方法:
- 提出了一种创新的,改进的实证模式分解 (EMD) 方法.
- 数字波器的设计是基于波信号的振幅频率特征.
- 使用过信号振幅一致性开发了EMD的分解停止标准.
主要成果:
- 改进的EMD方法有效过高频噪声,并调整旋信号.
- 该方法通过自适应性分解信号,同时自动从复杂噪声中提取状信号.
- 证明了流量计的高精度和抗干扰性能.
结论:
- 提议的改进EMD方法为高精度流量测量提供了一种新的方法.
- 这种技术成功地提取了信号,即使存在大量的噪音,提高了可靠性.
- 该方法提高了流量计的性能,为具有挑战性的测量场景提供了强大的解决方案.
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