基于SSA-VMD改进的波段值功能的蒸汽陷的降噪
Shuxun Li1, Qian Zhao1, Jinwei Liu2
1School of Petrochemical Technology, Lanzhou University of Technology, Lanzhou 730050, China.
Sensors (Basel, Switzerland)
|March 17, 2025
概括
本研究介绍了用于蒸汽陷监控的先进降噪技术. 具有变量模态分解和波形值的子搜索算法显著提高了声信号清晰度,以更好地检测泄漏.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 声学 声学 在声学方面
背景情况:
- 蒸汽陷的性能对于高效的蒸汽系统运行和节能至关重要.
- 通过声辐射信号监测蒸汽陷的状况对于识别内部泄漏至关重要.
- 环境噪音干扰了用于蒸汽陷诊断的准确声学信号分析.
研究的目的:
- 开发一种有效的降噪方法,用于从蒸汽陷发出的声学排放信号.
- 为了提高在蒸汽陷内识别内部泄漏状态的准确性,尽管有背景噪声.
- 改进信号与噪声比,以便更可靠地监测蒸汽陷状况.
主要方法:
- 一种结合Sparrow Search Algorithm (SSA),变量模态分解 (VMD) 和改进的波形值的新型无线化方法.
- SSA优化了VMD参数 (分解水平K,惩罚因子α) 使用最小的包裹.
- 波段值消音应用于VMD分解模式,然后进行信号重建.
主要成果:
- 拟议的SSA-VMD方法有效地过了来自声辐射信号的背景噪声.
- 与传统方法相比,该技术显著提高了信号噪声比率.
- 减少平方根平均误差证明了蒸汽陷信号的优异降噪性能.
结论:
- 集成的SSA-VMD和波形值方法为消除蒸汽陷声学信号提供了强大的解决方案.
- 这种方法提高了基于声辐射的诊断对蒸汽陷内部泄漏的可靠性.
- 这些发现有助于提高蒸汽系统的能源效率和运行完整性.
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