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Updated: Aug 27, 2025

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A Novel Air-Door Opening and Closing Identification Algorithm Using a Single Wind-Velocity Sensor.

Wentian Shang1,2, Lijun Deng1,2, Jian Liu1,2

  • 1College of Safety Science and Engineering, Liaoning Technical University, Huludao 125105, China.

Sensors (Basel, Switzerland)
|September 23, 2022
PubMed
Summary

This study introduces a novel method using wind velocity data to accurately identify mine air-door operations, reducing false alarms. The approach enhances mine safety by reliably detecting air-door status changes.

Keywords:
air-door opening and closingmachine learningtunnel ventilationwind-velocity data fluctuationwind-velocity monitoring

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Area of Science:

  • Mining Engineering
  • Sensor Technology
  • Machine Learning

Background:

  • Mine air-doors are critical for airflow regulation but their rapid open/close cycles can trigger false alarms with traditional sensors.
  • Existing switching sensors cannot correlate air-door status with wind speed fluctuations, leading to unreliable safety monitoring.
  • Abnormal wind velocity fluctuations during air-door operation can be misinterpreted, posing safety risks in mines.

Purpose of the Study:

  • To develop a robust method for identifying mine air-door opening and closing events using only a single wind-velocity sensor.
  • To overcome the limitations of switching sensors and mitigate false alarms caused by wind speed variations.
  • To enhance the safety and operational efficiency of mines through accurate air-door status detection.

Main Methods:

  • Utilized a multi-scale sliding window (MSSW) to segment wind-velocity sensor data.
  • Extracted global and fluctuation features using statistical methods and discrete wavelet transform (DWT).
  • Employed a machine learning model (Gradient Boosting Decision Tree - GBDT) for classification and non-maximum suppression for result merging.

Main Results:

  • The proposed algorithm achieved high accuracy, precision, and recall rates (91.89%, 93.07%, 91.07% respectively) on a dataset of air-door operations.
  • Performance remained strong (89.61%, 90.31%, 88.39% accuracy, precision, recall) even when including other mine production activities in the dataset.
  • The Gradient Boosting Decision Tree (GBDT) model demonstrated superior performance for classifying air-door events.

Conclusions:

  • The developed single wind-velocity sensor method effectively and accurately identifies mine air-door status, significantly reducing false alarms.
  • This algorithm offers a reliable and cost-effective solution for monitoring mine ventilation systems and improving overall safety.
  • The findings have practical implications for preventing accidents related to air-door malfunctions in active mining operations.