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一个基于VMD和IMF功率频谱的快速DAS信号分类算法高斯适配.
Haitao Liu1, Yunfan Xu1, Yuefeng Qi1
1School of Information Science and Engineering, The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, Yanshan University, Qinhuangdao, 066004, China.
Scientific reports
|October 9, 2025
概括
本研究引入了一种结合变化模式分解 (VMD) 和模态功率光谱密度 (PSD) 的新方法,用于分析分布式声学传感 (DAS) 数据. 该方法有效地提取振动信号特征,改善DAS系统中的智能监控和诊断.
科学领域:
- 工程 工程师 工程师 工程师
- 信号处理 信号处理
- 物理 物理学 物理
背景情况:
- 分布式声学传感 (DAS) 系统使用光纤来实时监控各种应用.
- 传统的功率光谱密度 (PSD) 分析与高频噪声作斗争,阻碍了准确的信号频率识别.
- 有效的特征提取对于DAS系统中准确的状态识别和诊断至关重要.
研究的目的:
- 为DAS振动信号开发一种新的光谱特征提取方法.
- 在杂环境中克服传统PSD分析的局限性.
- 通过深度学习实现对振动信号状态的智能识别和诊断.
主要方法:
- 提出了变化模式分解 (VMD) 和模态功率光谱密度 (PSD) 的联合方法.
- 航天器械分解振动信号以过噪音并提取有效的信号.
- 模态PSD提取子模态光谱特征,配备高斯函数以创建特征描述器矩阵.
主要成果:
- 从煤运输机滚筒振动数据中成功提取了轴承的光谱特征.
- 验证了VMD-PSD方法在表征振动源方面的有效性.
- 证明了将原始振动数据替换为深度学习的紧特征矩阵的潜力.
结论:
- 拟议的VMD-PSD方法为DAS系统中的智能监控提供了一个新的技术解决方案.
- 这种方法适用于对振动负载响应的评估.
- 该方法通过有效处理噪声干扰来提高信号分析的准确性.
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