DOI: 10.11799/ce202312005
Early warning technology of coal mine roof pressure based on machine learning
LU Zhenlong
XU Gang
YIN Xiwen
LIU Qianjin
Abstract：To address the issues of low reliability,poor accuracy and insufficient intelligence of the existing coal mine roof rock pressure early warning technology,we investigated a machine learning-based approach for analyzing roof pressure warning indicators on the working face.Linear regression and systematic clustering methods were employed to analyze the cyclic internal load of the hydraulic support and the periodic weighting interval,respectively.A roof pressure analysis and warning model was constructed to enable automatic analysis and prediction of key indicators for roof disaster monitoring on the working face.Field application results demonstrated that the warning system exhibited high reliability,comprehensive analysis and warning functionality,and high accuracy.In the statistical analysis of roof periodic weighting actual occurrences in the demonstration mine,the prediction accuracy was no less than 90%.This system can provide important support for the prevention and control of roof disasters in coal mines.
Keywords：roof disastermine pressure early warningmachine learningnon-pressure holding rateperiodic weightingcontour cloud map
Publication Date：2023-12-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 22-27 )