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相关概念视频

Discrete Fourier Transform01:15

Discrete Fourier Transform

303
The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
303

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相关实验视频

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Fiber Optic Distributed Sensors for High-resolution Temperature Field Mapping
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使用基于SST的Φ-OTDR系统进行振动事件识别.

Ruixu Yao1,2, Jun Li1,2, Jiarui Zhang1,2

  • 1School of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an 710054, China.

Sensors (Basel, Switzerland)
|November 14, 2023
PubMed
概括

本研究介绍了一种同步挤压转换 (SST) 方法,用于在相敏光学时域反射计 (Φ-OTDR) 系统中增强振动分析. 拟议的方法在识别各种振动事件方面实现了高精度,优于现有技术.

科学领域:

  • 光学工程是指光学工程.
  • 信号处理 信号处理
  • 机器学习 机器学习

背景情况:

  • 阶段灵敏光学时域反射计 (Φ-OTDR) 系统对于检测物理事件至关重要.
  • 准确的振动事件分析和识别对于强大的Φ-OTDR性能至关重要.
  • 现有的方法,如连续波形变换 (CWT) 和短时间里埃变换 (STFT),在时间频率分辨率和相位信息提取方面存在局限性.

研究的目的:

  • 提出和评估一种基于同步挤压转换 (SST) 的新方法,用于 Φ-OTDR 中的振动事件分析.
  • 提高时间频率分辨率和相位信息,以区分微妙的振动事件.
  • 评估深度学习分类器 (VGG,ViT,ResNet) 在应用于SST转换的 Φ-OTDR数据时的性能.

主要方法:

  • 将同步挤压转换 (SST) 应用于 Φ-OTDR 信号以改善时间频率表示.
  • 利用包括视觉几何组 (VGG),视觉变压器 (ViT) 和残余网络 (ResNet) 在内的深度学习模型进行分类.
  • 使用不同强度和衰减水平的多种振动事件进行实验验证.

主要成果:

  • 与基于CWT和STFT的方法相比,建议的SST方法显示出更高的性能.
  • 剩余网络 (ResNet) 成为分析数据的最有效的深度学习分类器.
关键词:
这是分类分类的分类.分布式纤维振动分布式纤维振动振动信号是一个振动信号.

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  • 通过不同的信号强度,事件类型和衰减条件,实现了高识别率.

    • 结论:

    • 基于SST的方法在Φ-OTDR系统中为振动事件分析和识别提供了显著的优势.
    • 通过将SST与深度学习分类器 (尤其是ResNet) 集成,为强大的 Φ-OTDR 监控提供了一个强大的工具.
    • 这种方法对于提高 Φ-OTDR 应用程序的可靠性和准确性具有相当大的价值.