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A rapid DAS signal classification algorithm based on VMD and IMF power spectrum Gaussian fitting.

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.

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|October 9, 2025
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Summary

This study introduces a new method combining variational mode decomposition (VMD) and modal power spectral density (PSD) for analyzing distributed acoustic sensing (DAS) data. The approach effectively extracts vibration signal features, improving intelligent monitoring and diagnosis in DAS systems.

Keywords:
Distributed acoustic sensing (DAS)Feature extractionMachine learningPower spectral sensity (PSD)Variational mode decomposition (VMD)

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Area of Science:

  • Engineering
  • Signal Processing
  • Physics

Background:

  • Distributed Acoustic Sensing (DAS) systems use optical fibers for real-time monitoring across various applications.
  • Traditional Power Spectral Density (PSD) analysis struggles with high-frequency noise, hindering accurate signal frequency identification.
  • Effective feature extraction is crucial for accurate state recognition and diagnosis in DAS systems.

Purpose of the Study:

  • To develop a novel spectral feature extraction method for DAS vibration signals.
  • To overcome limitations of traditional PSD analysis in noisy environments.
  • To enable intelligent recognition and diagnosis of vibration signal states using deep learning.

Main Methods:

  • Proposed a combined method of Variational Mode Decomposition (VMD) and modal Power Spectral Density (PSD).
  • VMD decomposes vibration signals to filter noise and extract valid signals.
  • Modal PSD extracts sub-mode spectral characteristics, fitted with Gaussian functions to create a feature descriptor matrix.

Main Results:

  • Successfully extracted bearing spectral features from coal conveyor roller vibration data.
  • Validated the effectiveness of the VMD-PSD method in characterizing vibration sources.
  • Demonstrated the potential for replacing raw vibration data with a compact feature matrix for deep learning.

Conclusions:

  • The proposed VMD-PSD method offers a novel technical solution for intelligent monitoring in DAS systems.
  • This approach is suitable for vibration load response evaluation.
  • The method enhances the accuracy of signal analysis by effectively handling noise interference.