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A DAS-Based Multi-Sensor Fusion Framework for Feature Extraction and Quantitative Blockage Monitoring in Coal Gangue

Chenyang Ma1, Jing Chai1,2, Dingding Zhang1,2

  • 1Energy and Mining Engineering College, Xi'an University of Science and Technology, Xi'an 710054, China.

Sensors (Basel, Switzerland)
|April 14, 2026
PubMed
Summary

A new multi-sensor method using distributed acoustic sensing (DAS), fiber Bragg grating (FBG), and accelerometers accurately detects and quantifies blockages in coal gangue slurry pipelines. This system enhances safety and efficiency in underground mining operations.

Keywords:
blockage identificationcoal gangue slurry pipelinedistributed acoustic sensing (DAS)multi-sensor collaborative monitoringquantitative characterization

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Area of Science:

  • Geotechnical Engineering
  • Mining Engineering
  • Sensor Technology

Background:

  • Long-distance coal gangue slurry pipelines are vital for underground coal mine green backfilling.
  • Pipeline blockages pose significant risks to operational safety and efficiency.
  • Existing monitoring methods, like distributed acoustic sensing (DAS), have limitations in accuracy and quantitative assessment.

Purpose of the Study:

  • To develop a novel feature-level fusion monitoring method for accurate blockage identification and quantitative evaluation in coal gangue slurry pipelines.
  • To address limitations of existing DAS-based monitoring, including insufficient accuracy and lack of specific indicators.
  • To establish a robust foundation for real-time early warning and precise diagnosis of pipeline blockages.

Main Methods:

  • Integration of distributed acoustic sensing (DAS), fiber Bragg grating (FBG), and piezoelectric accelerometers for a multi-sensor monitoring system.
  • Development of a slurry pipeline circulation test platform with simulated blockages (0% to 76.42%).
  • Multi-domain signal analysis to identify blockage-correlated characteristic frequencies (1.5 Hz, 26 Hz, 174 Hz) and development of a sine-fitting quantitative inversion model.

Main Results:

  • Identification and cross-validation of three key blockage-correlated frequencies: 1.5 Hz, 26 Hz (fluid-structure resonance), and 174 Hz (transient flow impact).
  • The DAS phase change rate showed a unimodal nonlinear response to blockage degree, peaking at 40.94% blockage.
  • A quantitative inversion model achieved high accuracy (R² = 0.985) and robustness (mean relative error = 3.77%) through cross-validation.

Conclusions:

  • The proposed multi-sensor fusion method enables accurate identification and quantitative assessment of blockages in coal gangue slurry pipelines.
  • A collaborative monitoring framework provides full-process blockage monitoring, including localization, severity calibration, and early warning.
  • The method offers significant engineering value for improving the safety and efficiency of underground coal mine green backfilling systems.